Methods and Devices for Detecting Diabetic Nephropathy and Associated Disorders

ABSTRACT

Methods and devices for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder in a mammal are described. In particular, methods and devices for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal are described.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Ser. No. 14/643,873, filed Mar.10, 2015, which is a continuation of Ser. No. 12/852,282, filed Aug. 6,2010, pending, which claims the priority of U.S. provisional applicationSer. No. 61/327,389, filed Apr. 23, 2010, and U.S. provisionalapplication Ser. No. 61/232,091, filed Aug. 7, 2009, each of which ishereby incorporated by reference in its entirety and is related to U.S.patent application Ser. Nos. 12/852,152; 12/852,202; 12/852,236;12/852,295; 12/852,312; and Ser. No. 12/852,322, the entire contents ofwhich are incorporated herein by reference.

FIELD OF THE INVENTION

The invention encompasses methods and devices for diagnosing,monitoring, or determining diabetic nephropathy or an associateddisorder in a mammal. In particular, the present invention providesmethods and devices for diagnosing, monitoring, or determining diabeticnephropathy or an associated disorder using measured concentrations of acombination of three or more analytes in a test sample taken from themammal.

BACKGROUND OF THE INVENTION

The urinary system, in particular the kidneys, perform several criticalfunctions such as maintaining electrolyte balance and eliminating toxinsfrom the bloodstream. In the human body, the pair of kidneys togetherprocess roughly 20% of the total cardiac output, amounting to about 1L/min in a 70-kg adult male. Because compounds in circulation areconcentrated in the kidney up to 1000-fold relative to the plasmaconcentration, the kidney is especially vulnerable to injury due toexposure to toxic compounds.

Diabetic nephropathy is the most common cause of chronic kidney failureand end-stage kidney disease in the United States. People with both type1 and type 2 diabetes are at risk. Existing diagnostic tests such as BUNand serum creatine tests typically detect only advanced stages of kidneydamage. Other diagnostic tests such as kidney tissue biopsies or CATscans have the advantage of enhanced sensitivity to earlier stages ofkidney damage, but these tests are also generally costly, slow, and/orinvasive.

A need exists in the art for a fast, simple, reliable, and sensitivemethod of detecting diabetic nephropathy or an associated disorder. In aclinical setting, the early detection of kidney damage would helpmedical practitioners to diagnose and treat kidney damage more quicklyand effectively.

SUMMARY OF THE INVENTION

The present invention provides methods and devices for diagnosing,monitoring, or determining a renal disorder in a mammal. In particular,the present invention provides methods and devices for diagnosing,monitoring, or determining a renal disorder using measuredconcentrations of a combination of three or more analytes in a testsample taken from the mammal.

One aspect of the invention encompasses a method for diagnosing,monitoring, or determining diabetic nephropathy or an associateddisorder in a mammal. The method typically comprises providing a testsample comprising a sample of bodily fluid taken from the mammal. Then,the method comprises determining a combination of sample concentrationsfor three or more sample analytes in the test sample, wherein the sampleanalytes are selected from the group consisting of alpha-1microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF,creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL,osteopontin, THP, TIMP-1, TFF-3, and VEGF. The combination of sampleconcentrations may be compared to a data set comprising at least oneentry, wherein each entry of the data set comprises a list comprisingthree or more minimum diagnostic concentrations indicative of diabeticnephropathy or an associated disorder. Each minimum diagnosticconcentration comprises a maximum of a range of analyte concentrationsfor a healthy mammal. Next, the method comprises determining a matchingentry of the dataset in which all minimum diagnostic concentrations areless than the corresponding sample concentrations and identifying anindicated disorder comprising the particular disorder of the matchingentry.

Another aspect of the invention encompasses a method for diagnosing,monitoring, or determining diabetic nephropathy or an associateddisorder in a mammal. The method generally comprises providing a testsample comprising a sample of bodily fluid taken from the mammal. Thenthe method comprises determining the concentrations of three or moresample analytes in a panel of biomarkers in the test sample, wherein thesample analytes are selected from the group consisting of alpha-1microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF,creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL,osteopontin, THP, TIMP-1, TFF-3, and VEGF. Diagnostic analytes areidentified in the test sample, wherein the diagnostic analytes are thesample analytes whose concentrations are statistically different fromconcentrations found in a control group of humans who do not suffer fromdiabetic nephropathy or an associated disorder. The combination ofdiagnostic analytes is compared to a dataset comprising at least oneentry, wherein each entry of the dataset comprises a combination ofthree or more diagnostic analytes reflective of diabetic nephropathy oran associated disorder. The particular disorder having the combinationof diagnostic analytes that essentially match the combination of sampleanalytes is then identified.

An additional aspect of the invention encompasses a method fordiagnosing, monitoring, or determining diabetic nephropathy or anassociated disorder in a mammal. The method usually comprises providingan analyte concentration measurement device comprising three or moredetection antibodies. Each detection antibody comprises an antibodycoupled to an indicator, wherein the antigenic determinants of theantibodies are sample analytes associated with diabetic nephropathy oran associated disorder. The sample analytes are generally selected fromthe group consisting of alpha-1 microglobulin, beta-2 microglobulin,calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1,microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. Themethod next comprises providing a test sample comprising three or moresample analytes and a bodily fluid taken from the mammal. The testsample is contacted with the detection antibodies and the detectionantibodies are allowed to bind to the sample analytes. Theconcentrations of the sample analytes are determined by detecting theindicators of the detection antibodies bound to the sample analytes inthe test sample. The concentrations of each sample analyte correspond toa corresponding minimum diagnostic concentration reflective of diabeticnephropathy or an associated disorder.

Other aspects and iterations of the invention are described in moredetail below.

DESCRIPTION OF FIGURES

FIG. 1 shows the four different disease groups from which samples wereanalyzed, and a plot of two different estimations on eGFR outlining thedistribution within each group.

FIG. 2A is a number of scatter plots of results on selected proteins inurine and plasma. The various groups are indicated as follows—control:blue, AA: red, DN: green, GN: yellow, OU: orange. (A) A1M in plasma, (B)cystatin C in plasma,

FIG. 2B is a number of scatter plots of results on selected proteins inurine and plasma. The various groups are indicated as follows—control:blue, AA: red, DN: green, GN: yellow, OU: orange. (C) B2M in urine, (D)cystatin C in urine.

FIG. 3 depicts the multivariate analysis of the disease groups and theirrespective matched controls using plasma results. Relative importanceshown using the random forest model.

FIG. 4A depicts a graph showing the mean AUROC and its standarddeviation for plasma samples, and mean error rates

FIG. 4B depicts a graph showing the mean AUROC and its standarddeviation and mean AUROC

FIG. 4C depicts a graph showing the mean AUROC and its standarddeviation from urine samples for each classification method used todistinguish disease samples vs. normal samples. Disease encompassesanalgesic abuse (AA), glomerulonephritis (GN), obstructive uropathy(OU), and diabetic nephropathy (DN). Normal=NL.

FIG. 5A depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 5A and FIG. 5B) or boosting (FIG. 5C) to distinguish disease(AA+GN+ON+DN) samples vs. normal samples from plasma (FIG. 5A) and urine(FIG. 5B and FIG. 5C).

FIG. 5B depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 5A and FIG. 5B) or boosting (FIG. 5C) to distinguish disease(AA+GN+ON+DN) samples vs. normal samples from plasma (FIG. 5A) and urine(FIG. 5B and FIG. 5C).

FIG. 5C depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 5A and FIG. 5B) or boosting (FIG. 5C) to distinguish disease(AA+GN+ON+DN) samples vs. normal samples from plasma (FIG. 5A) and urine(FIG. 5B and FIG. 5C).

FIG. 6A depicts a graph showing the mean AUROC and its standarddeviation for plasma samples, and mean error rates

FIG. 6B depicts a graph showing the mean AUROC and its standarddeviation and mean AUROC

FIG. 6C depicts a graph showing the mean AUROC and its standarddeviation from urine samples for each classification method used todistinguish diabetic nephropathy samples vs. normal samples.Abbreviations as in FIG. 4.

FIG. 7A depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 7A and FIG. 7B) or boosting (FIG. 7C) to distinguish diabeticnephropathy samples vs. normal samples from plasma (FIG. 7A) and urine(FIG. 7B and FIG. 7C).

FIG. 7B depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 7A and FIG. 7B) or boosting (FIG. 7C) to distinguish diabeticnephropathy samples vs. normal samples from plasma (FIG. 7A) and urine(FIG. 7B and FIG. 7C).

FIG. 7C depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 7A and FIG. 7B) or boosting (FIG. 7C) to distinguish diabeticnephropathy samples vs. normal samples from plasma (FIG. 7A) and urine(FIG. 7B and FIG. 7C).

FIG. 8A depicts a graph showing the mean AUROC and its standarddeviation for plasma samples, and mean error rates

FIG. 8B depicts a graph showing the mean AUROC and its standarddeviation and mean AUROC

FIG. 8C depicts a graph showing the mean AUROC and its standarddeviation from urine samples for each classification method used todistinguish analgesic abuse samples vs. diabetic nephropathy samples.Abbreviations as in FIG. 4.

FIG. 9A depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 9A and FIG. 9B) or boosting (FIG. 9C) to distinguish analgesicabuse samples vs. diabetic nephropathy samples from plasma (FIG. 9A) andurine (FIG. 9B and FIG. 9C).

FIG. 9B depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 9A and FIG. 9B) or boosting (FIG. 9C) to distinguish analgesicabuse samples vs. diabetic nephropathy samples from plasma (FIG. 9A) andurine (FIG. 9B and FIG. 9C).

FIG. 9C depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 9A and FIG. 9B) or boosting (FIG. 9C) to distinguish analgesicabuse samples vs. diabetic nephropathy samples from plasma (FIG. 9A) andurine (FIG. 9B and FIG. 9C).

FIG. 10A depicts a graph showing the mean AUROC and its standarddeviation for plasma samples, and mean error rates

FIG. 10B depicts a graph showing the mean AUROC and its standarddeviation and mean AUROC

FIG. 10C depicts a graph showing the mean AUROC and its standarddeviation from urine samples for each classification method used todistinguish obstructive uropathy samples vs. diabetic nephropathysamples. Abbreviations as in FIG. 4.

FIG. 11A depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 11A and FIG. 11B) or boosting (FIG. 11C) to distinguishobstructive uropathy samples vs. diabetic nephropathy samples fromplasma (FIG. 11A) and urine (FIG. 11B and FIG. 11C).

FIG. 11B depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 11A and FIG. 11B) or boosting (FIG. 11C) to distinguishobstructive uropathy samples vs. diabetic nephropathy samples fromplasma (FIG. 11A) and urine (FIG. 11B and FIG. 11C).

FIG. 11C depicts a graph showing the average importance of analytes andclinical variables from 100 bootstrap runs measured by random forest(FIG. 11A and FIG. 11B) or boosting (FIG. 11C) to distinguishobstructive uropathy samples vs. diabetic nephropathy samples fromplasma (FIG. 11A) and urine (FIG. 11B and FIG. 11C).

FIG. 12A depicts a graph showing the mean AUROC and its standarddeviation for plasma samples, and mean error rates

FIG. 12B depicts a graph showing the mean AUROC and its standarddeviation and mean AUROC

FIG. 12C depicts a graph showing the mean AUROC and its standarddeviation from urine samples for each classification method used todistinguish diabetic nephropathy samples vs. glomerulonephritis samples.Abbreviations as in FIG. 4.

FIG. 13A depicts a graph showing the average importance of analytes andclinical variables from I 00 bootstrap runs measured by random forest(FIG. 13A and FIG. 13B) or boosting (FIG. 13C) to distinguish diabeticnephropathy samples vs. glomerulonephritis samples from plasma (FIG.13A) and urine (FIG. 13B and FIG. 13C).

FIG. 13B depicts a graph showing the average importance of analytes andclinical variables from I 00 bootstrap runs measured by random forest(FIG. 13A and FIG. 13B) or boosting (FIG. 13C) to distinguish diabeticnephropathy samples vs. glomerulonephritis samples from plasma (FIG.13A) and urine (FIG. 13B and FIG. 13C).

FIG. 13C depicts a graph showing the average importance of analytes andclinical variables from I 00 bootstrap runs measured by random forest(FIG. 13A and FIG. 13B) or boosting (FIG. 13C) to distinguish diabeticnephropathy samples vs. glomerulonephritis samples from plasma (FIG.13A) and urine (FIG. 13B and FIG. 13C).

FIG. 14A depicts several graphs illustrating the linear correlationbetween an analyte and years diagnosed with diabetes. Red=cases;Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C) calbindin, (D) clusteri;FIG. 14B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 14C:(I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 14D (M)THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 14B depicts several graphs illustrating the linear correlationbetween an analyte and years diagnosed with diabetes. Red=cases;Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C) calbindin, (D) clusteri;FIG. 14B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 14C:(I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 14D (M)THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 14C depicts several graphs illustrating the linear correlationbetween an analyte and years diagnosed with diabetes. Red=cases;Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C) calbindin, (D) clusteri;FIG. 14B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 14C:(I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 14D (M)THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 14D depicts several graphs illustrating the linear correlationbetween an analyte and years diagnosed with diabetes. Red=cases;Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C) calbindin, (D) clusteri;FIG. 14B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 14C:(I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 14D (M)THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 15A depicts several graphs illustrating the log correlation betweenan analyte and years diagnosed with diabetes. Red=cases; Black=controls.FIG. 15A: (A) A1M, (B) B2M, (C) calbindin, (D) clusterin; FIG. 15B: (E)CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 15C: (I) KIM-I,(J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N)TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 15B depicts several graphs illustrating the log correlation betweenan analyte and years diagnosed with diabetes. Red=cases; Black=controls.FIG. 15A: (A) A1M, (B) B2M, (C) calbindin, (D) clusterin; FIG. 15B: (E)CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 15C: (I) KIM-I,(J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N)TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 15C depicts several graphs illustrating the log correlation betweenan analyte and years diagnosed with diabetes. Red=cases; Black=controls.FIG. 15A: (A) A1M, (B) B2M, (C) calbindin, (D) clusterin; FIG. 15B: (E)CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 15C: (I) KIM-I,(J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N)TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 15D depicts several graphs illustrating the log correlation betweenan analyte and years diagnosed with diabetes. Red=cases; Black=controls.FIG. 15A: (A) A1M, (B) B2M, (C) calbindin, (D) clusterin; FIG. 15B: (E)CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 15C: (I) KIM-I,(J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N)TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 16A depicts several graphs illustrating the log correlation betweenan analyte and clinical 24 hr microalbumin (A) A1M, (B) B2M, (C)calbindin, (D) clusterin;

FIG. 16B depicts several graphs illustrating the log correlation betweenan analyte and clinical 24 hr microalbumin (E) CTGF, (F) creatinine, (G)cystatin C, (H) GST α;

FIG. 16C depicts several graphs illustrating the log correlation betweenan analyte and clinical 24 hr microalbumin (I) KIM-I, (J) microalbumin,(K) NGAL, (L) osteopontin;

FIG. 16D depicts several graphs illustrating the log correlation betweenan analyte and clinical 24 hr microalbumin (M) THP, (N) TIMP-1, (O)TFF-3, and (P) VEGF.

FIG. 17 A depicts several graphs illustrating the linear correlationbetween an analyte and clinical 24 hr microalbumin. (A) A1M, (B) B2M,(C) calbindin, (D) clusterin;

FIG. 17B depicts several graphs illustrating the linear correlationbetween an analyte and clinical 24 hr microalbumin. (E) CTGF, (F)creatinine, (G) cystatin C, (H) GST α;

FIG. 17C depicts several graphs illustrating the linear correlationbetween an analyte and clinical 24 hr microalbumin. (I) KIM-I, (J)microalbumin, (K) NGAL, (L) osteopontin

FIG. 17D depicts several graphs illustrating the linear correlationbetween an analyte and clinical 24 hr microalbumin. (M) THP, (N) TIMP-1,(O) TFF-3, and (P) VEGF.

FIG. 18A depicts several graphs illustrating linear cdplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin;

FIG. 18B depicts several graphs illustrating linear cdplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α;

FIG. 18C depicts several graphs illustrating linear cdplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;

FIG. 18D depicts several graphs illustrating linear cdplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 19A depicts several graphs illustrating log cdplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin;

FIG. 19B depicts several graphs illustrating log cdplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α;

FIG. 19C depicts several graphs illustrating log cdplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;

FIG. 19D depicts several graphs illustrating log cdplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 20A depicts several graphs illustrating linear qqplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin;

FIG. 20B depicts several graphs illustrating linear qqplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α;

FIG. 20C depicts several graphs illustrating linear qqplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;

FIG. 20D depicts several graphs illustrating linear qqplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 21A depicts several graphs illustrating log qqplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin;

FIG. 21B depicts several graphs illustrating log qqplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α;

FIG. 21C depicts several graphs illustrating log qqplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;

FIG. 21D depicts several graphs illustrating log qqplots of urineanalytes compared to diabetic disease. Levels were normalized to urinecreatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 22A depicts several graphs illustrating linear stripcharts of urineanalytes compared to diabetic kidney disease (KD) or diabetic patientswithout kidney disease controls (NC). Levels were normalized to urinecreatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin, (E) CTGF,(F) creatinine;

FIG. 22B depicts several graphs illustrating linear stripcharts of urineanalytes compared to diabetic kidney disease (KD) or diabetic patientswithout kidney disease controls (NC). Levels were normalized to urinecreatinine. (G) cystatin C, (H) GST α, (I) KIM-I, (J) microalbumin, (K)NGAL, (L) osteopontin;

FIG. 22C depicts several graphs illustrating linear stripcharts of urineanalytes compared to diabetic kidney disease (KD) or diabetic patientswithout kidney disease controls (NC). Levels were normalized to urinecreatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 23A depicts several graphs illustrating log stripcharts of urineanalytes compared to diabetic kidney disease (KD) or diabetic patientswithout kidney disease controls (NC). Levels were normalized to urinecreatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin, (E) CTGF,(F) creatinine;

FIG. 23B depicts several graphs illustrating log stripcharts of urineanalytes compared to diabetic kidney disease (KD) or diabetic patientswithout kidney disease controls (NC). Levels were normalized to urinecreatinine. (G) cystatin C, (H) GST α, (I) KIM-I, (J) microalbumin, (K)NGAL, (L) osteopontin;

FIG. 23C depicts several graphs illustrating log stripcharts of urineanalytes compared to diabetic kidney disease (KD) or diabetic patientswithout kidney disease controls (NC). Levels were normalized to urinecreatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 24 depicts a graph illustrating years diagnosed v. disease.

FIG. 25A depicts several graphs illustrating linear stripcharts of serumanalytes compared to diabetic kidney disease (KD) or diabetic patientswithout kidney disease controls (NC). (A) A1M, (B) B2M, (C) clusterin,(D) CTGF, (E) cystatin C, (F) GST α;

FIG. 25B depicts several graphs illustrating linear stripcharts of serumanalytes compared to diabetic kidney disease (KD) or diabetic patientswithout kidney disease controls (NC). (G) KIM-I, (H) NGAL, (I)osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1; and

FIG. 25C depicts a graph illustrating linear stripcharts of serumanalytes compared to diabetic kidney disease (KD) or diabetic patientswithout kidney disease controls (NC). (M) VEGF.

FIG. 26A depicts several graphs illustrating log stripcharts of serumanalytes compared to diabetic kidney disease. (A) A1M, (B) B2M;

FIG. 26B depicts several graphs illustrating log stripcharts of serumanalytes compared to diabetic kidney disease. (C) clusterin, (D) CTGF,(E) cystatin C, (F) GST α, (G) KIM-I, (H) NGAL;

FIG. 26C depicts several graphs illustrating log stripcharts of serumanalytes compared to diabetic kidney disease. (I) osteopontin, (J)TFF-3, (K) THP, (L) TIMP-1, and (M) VEGF.

FIG. 27A depicts several graphs illustrating linear qqplots of serumanalytes compared to diabetic kidney disease. (A) A1M, (B) B2M, (C)clusterin, (D) CTGF;

FIG. 27B depicts several graphs illustrating linear qqplots of serumanalytes compared to diabetic kidney disease. (E) cystatin C, (F) GST α,(G) KIM-I, (H) NGAL;

FIG. 27C depicts several graphs illustrating linear qqplots of serumanalytes compared to diabetic kidney disease. (I) osteopontin, (J)TFF-3, (K) THP, (L) TIMP-1; and

FIG. 27D depicts a graph illustrating linear qqplots of serum analytescompared to diabetic kidney disease. (M) VEGF.

FIG. 28A depicts several graphs illustrating log qqplots of serumanalytes compared to diabetic kidney disease. (A) A1M, (B) B2M;

FIG. 28B depicts several graphs illustrating log qqplots of serumanalytes compared to diabetic kidney disease. (C) clusterin, (D) CTGF,(E) cystatin C, (F) GST α;

FIG. 28C depicts several graphs illustrating log qqplots of serumanalytes compared to diabetic kidney disease. (G) KIM-I, (H) NGAL, (I)osteopontin, (J) TFF-3:

FIG. 28D depicts several graphs illustrating log qqplots of serumanalytes compared to diabetic kidney disease. (K) THP, (L) TIMP-1, and(M) VEGF.

FIG. 29A depicts several graphs illustrating a linear comparison ofanalytes v. years diagnosed. Red=cases; Black=controls. (A) A1M, (B)B2M, (C) clusterin, (D) CTGF;

FIG. 29B depicts several graphs illustrating a linear comparison ofanalytes v. years diagnosed. Red=cases; Black=controls. (E) cystatinC,(F) GST α, (G) KIM-I, (H) NGAL;

FIG. 29C depicts several graphs illustrating a linear comparison ofanalytes v. years diagnosed. Red=cases; Black=controls. (I) osteopontin,(J) TFF-3, (K) THP, (L) TIMP-1, and (M) VEGF.

FIG. 30A depicts several graphs illustrating a log comparison ofanalytes v. years diagnosed. Red=cases; Black=controls. (A) A1M, (B)B2M, (C) clusterin, (D) CTGF;

FIG. 30B depicts several graphs illustrating a log comparison ofanalytes v. years diagnosed. Red=cases; Black=controls. (E) cystatin C,(F) GST α, (G) KIM-I, (H) NGAL;

FIG. 30C depicts several graphs illustrating a log comparison ofanalytes v. years diagnosed. Red=cases; Black=controls. (I) osteopontin,(J) TFF-3, (K) THP, (L) TIMP-1, and (M) VEGF.

FIG. 31A depicts several graphs illustrating a linear comparison ofserum analytes v. clinical microalbumin. (A) A1M, (B) B2M, (C)clusterin, (D) CTGF;

FIG. 31B depicts several graphs illustrating a linear comparison ofserum analytes v. clinical microalbumin. (E) cystatin C, (F) GST α, (G)KIM-I, (H) NGAL

FIG. 31C depicts several graphs illustrating a linear comparison ofserum analytes v. clinical microalbumin. (I) osteopontin, (J) TFF-3, (K)THP, (L) TIMP-1; and

FIG. 31D depicts a graph illustrating a linear comparison of serumanalytes v. clinical microalbumin. (M) VEGF.

FIG. 32A depicts several graphs illustrating a log comparison of serumanalytes v. clinical microalbumin. (A) A1M, (B) B2M;

FIG. 32B depicts several graphs illustrating a log comparison of serumanalytes v. clinical microalbumin. (C) clusterin, (D) CTGF, (E) cystatinC, (F) GST α;

FIG. 32C depicts several graphs illustrating a log comparison of serumanalytes v. clinical microalbumin. (G) KIM-I, (H) NGAL, (I) osteopontin,(J) TFF-3;

FIG. 32D depicts several graphs illustrating a log comparison of serumanalytes v. clinical microalbumin. (K) THP, (L) TIMP-1, and (M) VEGF.

DETAILED DESCRIPTION OF THE INVENTION

It has been discovered that a multiplexed panel of at least three, six,or preferably 16 biomarkers may be used to detect diabetic nephropathyand associated disorders. As used herein, the term “diabeticnephropathy” refers to a disorder characterized by angiopathy ofcapillaries in the kidney glomeruli. The term encompassesKimmelstiel-Wilson syndrome, or nodular diabetic glomerulosclerosis andintercapillary glomerulonephritis. Additionally, the present inventionencompasses biomarkers that may be used to detect a disorder associatedwith diabetic nephropathy. As used herein, the phrase “a disorderassociated with diabetic nephropathy” refers to a disorder that stemsfrom angiopathy of capillaries in the kidney glomeruli. For instance,non-limiting examples of associated disorders may include nephriticsyndrome, chronic kidney failure, and end-stage kidney disease.

The biomarkers included in a multiplexed panel of the invention areanalytes known in the art that may be detected in the urine, serum,plasma and other bodily fluids of mammals. As such, the analytes of themultiplexed panel may be readily extracted from the mammal in a testsample of bodily fluid. The concentrations of the analytes within thetest sample may be measured using known analytical techniques such as amultiplexed antibody-based immunological assay. The combination ofconcentrations of the analytes in the test sample may be compared toempirically determined combinations of minimum diagnostic concentrationsand combinations of diagnostic concentration ranges associated withhealthy kidney function or diabetic nephropathy or an associateddisorder to determine whether diabetic nephropathy or an associateddisorder is indicated in the mammal.

One embodiment of the present invention provides a method fordiagnosing, monitoring, or determining diabetic nephropathy or anassociated disorder in a mammal that includes determining the presenceor concentration of a combination of three or more sample analytes in atest sample containing the bodily fluid of the mammal. The measuredconcentrations of the combination of sample analytes is compared to theentries of a dataset in which each entry contains the minimum diagnosticconcentrations of a combination of three of more analytes reflective ofdiabetic nephropathy or an associated disorder. Other embodimentsprovide computer-readable media encoded with applications containingexecutable modules, systems that include databases and processingdevices containing executable modules configured to diagnose, monitor,or determine a renal disorder in a mammal. Still other embodimentsprovide antibody-based devices for diagnosing, monitoring, ordetermining diabetic nephropathy or an associated disorder in a mammal.

The analytes used as biomarkers in the multiplexed assay, methods ofdiagnosing, monitoring, or determining a renal disorder usingmeasurements of the analytes, systems and applications used to analyzethe multiplexed assay measurements, and antibody-based devices used tomeasure the analytes are described in detail below.

I. Analytes in Multiplexed Assay

One embodiment of the invention measures the concentrations of three,six, or preferable sixteen biomarker analytes within a test sample takenfrom a mammal and compares the measured analyte concentrations tominimum diagnostic concentrations to diagnose, monitor, or determinediabetic nephropathy or an associated disorder in a mammal. In thisaspect, the biomarker analytes are known in the art to occur in theurine, plasma, serum and other bodily fluids of mammals. The biomarkeranalytes are proteins that have known and documented associations withearly renal damage in humans. As defined herein, the biomarker analytesinclude but are not limited to alpha-1 microglobulin, beta-2microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C,GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3,and VEGF. A description of each biomarker analyte is given below.

(a) Alpha-1 Microglobulin (A1M)

Alpha-1 microglobulin (A1M, Swiss-Prot Accession Number P02760) is a 26kDa glycoprotein synthesized by the liver and reabsorbed in the proximaltubules. Elevated levels of A1M in human urine are indicative ofglomerulotubular dysfunction. A1M is a member of the lipocalin superfamily and is found in all tissues. Alpha-1-microglobulin exists inblood in both a free form and complexed with immunoglobulin A (IgA) andheme. Half of plasma A1M exists in a free form, and the remainder existsin complexes with other molecules including prothrombin, albumin,immunoglobulin A and heme. Nearly all of the free A1M in human urine isreabsorbed by the megalin receptor in proximal tubular cells, where itis then catabolized. Small amounts of A1M are excreted in the urine ofhealthy humans. Increased A1M concentrations in human urine may be anearly indicator of renal damage, primarily in the proximal tubule.

(b) Beta-2 Microglobulin (B2M)

Beta-2 microglobulin (B2M, Swiss-Prot Accession Number P61769) is aprotein found on the surfaces of all nucleated cells and is shed intothe blood, particularly by tumor cells and lymphocytes. Due to its smallsize, B2M passes through the glomerular membrane, but normally less than1% is excreted due to reabsorption of B2M in the proximal tubules of thekidney. Therefore, high plasma levels of B2M occur as a result of renalfailure, inflammation, and neoplasms, especially those associated withB-lymphocytes.

(c) Calbindin

Calbindin (Calbindin D-28K, Swiss-Prot Accession Number P05937) is aCa-binding protein belonging to the troponin C superfamily. It isexpressed in the kidney, pancreatic islets, and brain. Calbindin isfound predominantly in subpopulations of central and peripheral nervoussystem neurons, in certain epithelial cells involved in Ca2+ transportsuch as distal tubular cells and cortical collecting tubules of thekidney, and in enteric neuroendocrine cells.

(d) Clusterin

Clusterin (Swiss-Prot Accession Number P10909) is a highly conservedprotein that has been identified independently by many differentlaboratories and named SGP2, S35-S45, apolipoprotein J, SP-40, 40,ADHC-9, gp80, GPIII, and testosterone-repressed prostate message(TRPM-2). An increase in clusterin levels has been consistently detectedin apoptotic heart, brain, lung, liver, kidney, pancreas, and retinaltissue both in vivo and in vitro, establishing clusterin as a ubiquitousmarker of apoptotic cell loss. However, clusterin protein has also beenimplicated in physiological processes that do not involve apoptosis,including the control of complement-mediated cell lysis, transport ofbeta-amyloid precursor protein, shuttling of aberrant beta-amyloidacross the blood-brain barrier, lipid scavenging, membrane remodeling,cell aggregation, and protection from immune detection and tumornecrosis factor induced cell death.

(e) Connective Tissue Growth Factor (CTGF)

Connective tissue growth factor (CTGF, Swiss-Prot Accession NumberP29279) is a 349-amino acid cysteine-rich polypeptide belonging to theCCN family. In vitro studies have shown that CTGF is mainly involved inextracellular matrix synthesis and fibrosis. Up-regulation of CTGF mRNAand increased CTGF levels have been observed in various diseases,including diabetic nephropathy and cardiomyopathy, fibrotic skindisorders, systemic sclerosis, biliary atresia, liver fibrosis andidiopathic pulmonary fibrosis, and nondiabetic acute and progressiveglomerular and tubulointerstitial lesions of the kidney. A recentcross-sectional study found that urinary CTGF may act as a progressionpromoter in diabetic nephropathy.

(f) Creatinine

Creatinine is a metabolite of creatine phosphate in muscle tissue, andis typically produced at a relatively constant rate by the body.Creatinine is chiefly filtered out of the blood by the kidneys, though asmall amount is actively secreted by the kidneys into the urine.Creatinine levels in blood and urine may be used to estimate thecreatinine clearance, which is representative of the overall glomerularfiltration rate (GFR), a standard measure of renal function. Variationsin creatinine concentrations in the blood and urine, as well asvariations in the ratio of urea to creatinine concentration in theblood, are common diagnostic measurements used to assess renal function.

(g) Cystatin C (Cyst C)

Cystatin C (Cyst C, Swiss-Prot Accession Number P01034) is a 13 kDaprotein that is a potent inhibitor of the C1 family of cysteineproteases. It is the most abundant extracellular inhibitor of cysteineproteases in testis, epididymis, prostate, seminal vesicles and manyother tissues. Cystatin C, which is normally expressed in vascular wallsmooth muscle cells, is severely reduced in both atherosclerotic andaneurismal aortic lesions.

(h) Glutathione S-Transferase Alpha (GST-Alpha)

Glutathione S-transferase alpha (GST-alpha, Swiss-Prot Accession NumberP08263) belongs to a family of enzymes that utilize glutathione inreactions contributing to the transformation of a wide range ofcompounds, including carcinogens, therapeutic drugs, and products ofoxidative stress. These enzymes play a key role in the detoxification ofsuch substances.

(i) Kidney Injury Molecule-1 (KIM-1)

Kidney injury molecule-1 (KIM-1, Swiss-Prot Accession Number Q96D42) isan immunoglobulin superfamily cell-surface protein highly upregulated onthe surface of injured kidney epithelial cells. It is also known asTIM-1 (T-cell immunoglobulin mucin domain-1), as it is expressed at lowlevels by subpopulations of activated T-cells and hepatitis A viruscellular receptor-1 (HAVCR-1). KIM-1 is increased in expression morethan any other protein in the injured kidney and is localizedpredominantly to the apical membrane of the surviving proximalepithelial cells.

(j) Microalbumin

Albumin is the most abundant plasma protein in humans and other mammals.Albumin is essential for maintaining the osmotic pressure needed forproper distribution of body fluids between intravascular compartmentsand body tissues. Healthy, normal kidneys typically filter out albuminfrom the urine. The presence of albumin in the urine may indicate damageto the kidneys. Albumin in the urine may also occur in patients withlong-standing diabetes, especially type 1 diabetes. The amount ofalbumin eliminated in the urine has been used to differentially diagnosevarious renal disorders. For example, nephrotic syndrome usually resultsin the excretion of about 3.0 to 3.5 grams of albumin in human urineevery 24 hours. Microalbuminuria, in which less than 300 mg of albuminis eliminated in the urine every 24 hours, may indicate the early stagesof diabetic nephropathy.

(k) Neutrophil Gelatinase-Associated Lipocalin (NGAL)

Neutrophil gelatinase-associated lipocalin (NGAL, Swiss-Prot AccessionNumber P80188) forms a disulfide bond-linked heterodimer with MMP-9. Itmediates an innate immune response to bacterial infection bysequestrating iron. Lipocalins interact with many different moleculessuch as cell surface receptors and proteases, and play a role in avariety of processes such as the progression of cancer and allergicreactions.

(l) Osteopontin (OPN)

Osteopontin (OPN, Swiss-Prot Accession Number P10451) is a cytokineinvolved in enhancing production of interferon-gamma and IL-12, andinhibiting the production of IL-10. OPN is essential in the pathway thatleads to type I immunity. OPN appears to form an integral part of themineralized matrix. OPN is synthesized within the kidney and has beendetected in human urine at levels that may effectively inhibit calciumoxalate crystallization. Decreased concentrations of OPN have beendocumented in urine from patients with renal stone disease compared withnormal individuals.

(m) Tamm-Horsfall Protein (THP)

Tamm-Horsfall protein (THP, Swiss-Prot Accession Number P07911), alsoknown as uromodulin, is the most abundant protein present in the urineof healthy subjects and has been shown to decrease in individuals withkidney stones. THP is secreted by the thick ascending limb of the loopof Henley. THP is a monomeric glycoprotein of ˜85 kDa with ˜30%carbohydrate moiety that is heavily glycosylated. THP may act as aconstitutive inhibitor of calcium crystallization in renal fluids.

(n) Tissue Inhibitor of Metalloproteinase-1 (TIMP-1)

Tissue inhibitor of metalloproteinase-1 (TIMP-1, Swiss-Prot AccessionNumber P01033) is a major regulator of extracellular matrix synthesisand degradation. A certain balance of MMPs and TIMPs is essential fortumor growth and health. Fibrosis results from an imbalance offibrogenesis and fibrolysis, highlighting the importance of the role ofthe inhibition of matrix degradation role in renal disease.

(o) Trefoil Factor 3 (TFF3)

Trefoil factor 3 (TFF3, Swiss-Prot Accession Number Q07654), also knownas intestinal trefoil factor, belongs to a small family ofmucin-associated peptides that include TFF1, TFF2, and TFF3. TFF3 existsin a 60-amino acid monomeric form and a 118-amino acid dimeric form.Under normal conditions TFF3 is expressed by goblet cells of theintestine and the colon. TFF3 expression has also been observed in thehuman respiratory tract, in human goblet cells and in the human salivarygland. In addition, TFF3 has been detected in the human hypothalamus.

(p) Vascular Endothelial Growth Factor (VEGF)

Vascular endothelial growth factor (VEGF, Swiss-Prot Accession NumberP15692) is an important factor in the pathophysiology of neuronal andother tumors, most likely functioning as a potent promoter ofangiogenesis. VEGF may also be involved in regulatingblood-brain-barrier functions under normal and pathological conditions.VEGF secreted from the stromal cells may be responsible for theendothelial cell proliferation observed in capillary hemangioblastomas,which are typically composed of abundant microvasculature and primitiveangiogenic elements represented by stromal cells.

II. Combinations of Analytes Measured by Multiplexed Assay

The method for diagnosing, monitoring, or determining a renal disorderinvolves determining the presence or concentrations of a combination ofsample analytes in a test sample. The combinations of sample analytes,as defined herein, are any group of three or more analytes selected fromthe biomarker analytes, including but not limited to alpha-1microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF,creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL,osteopontin, THP, TIMP-1, TFF-3, and VEGF. In one embodiment, thecombination of analytes may be selected to provide a group of analytesassociated with diabetic nephropathy or an associated disorder.

In one embodiment, the combination of sample analytes may be any threeof the biomarker analytes. In other embodiments, the combination ofsample analytes may be any four, any five, any six, any seven, anyeight, any nine, any ten, any eleven, any twelve, any thirteen, anyfourteen, any fifteen, or all sixteen of the sixteen biomarker analytes.In some embodiments, the combination of sample analytes comprisesalpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, andTIMP-1. In another embodiment, the combination of sample analytes may bea combination listed in Table A.

TABLE A alpha-1 microglobulin beta-2 microglobulin calbindin alpha-1microglobulin beta-2 microglobulin clusterin alpha-1 microglobulinbeta-2 microglobulin CTGF alpha-1 microglobulin beta-2 microglobulincreatinine alpha-1 microglobulin beta-2 microglobulin cystatin C alpha-1microglobulin beta-2 microglobulin GST-alpha alpha-1 microglobulinbeta-2 microglobulin KIM-1 alpha-1 microglobulin beta-2 microglobulinmicroalbumin alpha-1 microglobulin beta-2 microglobulin NGAL alpha-1microglobulin beta-2 microglobulin osteopontin alpha-1 microglobulinbeta-2 microglobulin THP alpha-1 microglobulin beta-2 microglobulinTIMP-1 alpha-1 microglobulin beta-2 microglobulin TFF-3 alpha-1microglobulin beta-2 microglobulin VEGF alpha-1 microglobulin calbindinclusterin alpha-1 microglobulin calbindin CTGF alpha-1 microglobulincalbindin creatinine alpha-1 microglobulin calbindin cystatin C alpha-1microglobulin calbindin GST-alpha alpha-1 microglobulin calbindin KIM-1alpha-1 microglobulin calbindin microalbumin alpha-1 microglobulincalbindin NGAL alpha-1 microglobulin calbindin osteopontin alpha-1microglobulin calbindin THP alpha-1 microglobulin calbindin TIMP-1alpha-1 microglobulin calbindin TFF-3 alpha-1 microglobulin calbindinVEGF alpha-1 microglobulin clusterin CTGF alpha-1 microglobulinclusterin creatinine alpha-1 microglobulin clusterin cystatin C alpha-1microglobulin clusterin GST-alpha alpha-1 microglobulin clusterin KIM-1alpha-1 microglobulin clusterin microalbumin alpha-1 microglobulinclusterin NGAL alpha-1 microglobulin clusterin osteopontin alpha-1microglobulin clusterin THP alpha-1 microglobulin clusterin TIMP-1alpha-1 microglobulin clusterin TFF-3 alpha-1 microglobulin clusterinVEGF alpha-1 microglobulin CTGF creatinine alpha-1 microglobulin CTGFcystatin C alpha-1 microglobulin CTGF GST-alpha alpha-1 microglobulinCTGF KIM-1 alpha-1 microglobulin CTGF microalbumin alpha-1 microglobulinCTGF NGAL alpha-1 microglobulin CTGF osteopontin alpha-1 microglobulinCTGF THP alpha-1 microglobulin CTGF TIMP-1 alpha-1 microglobulin CTGFTFF-3 alpha-1 microglobulin CTGF VEGF alpha-1 microglobulin creatininecystatin C alpha-1 microglobulin creatinine GST-alpha alpha-1microglobulin creatinine KIM-1 alpha-1 microglobulin creatininemicroalbumin alpha-1 microglobulin creatinine NGAL alpha-1 microglobulincreatinine osteopontin alpha-1 microglobulin creatinine THP alpha-1microglobulin creatinine TIMP-1 alpha-1 microglobulin creatinine TFF-3alpha-1 microglobulin creatinine VEGF alpha-1 microglobulin cystatin CGST-alpha alpha-1 microglobulin cystatin C KIM-1 alpha-1 microglobulincystatin C microalbumin alpha-1 microglobulin cystatin C NGAL alpha-1microglobulin cystatin C osteopontin alpha-1 microglobulin cystatin CTHP alpha-1 microglobulin cystatin C TIMP-1 alpha-1 microglobulincystatin C TFF-3 alpha-1 microglobulin cystatin C VEGF alpha-1microglobulin GST-alpha KIM-1 alpha-1 microglobulin GST-alphamicroalbumin alpha-1 microglobulin GST-alpha NGAL alpha-1 microglobulinGST-alpha osteopontin alpha-1 microglobulin GST-alpha THP alpha-1microglobulin GST-alpha TIMP-1 alpha-1 microglobulin GST-alpha TFF-3alpha-1 microglobulin GST-alpha VEGF alpha-1 microglobulin KIM-1microalbumin alpha-1 microglobulin KIM-1 NGAL alpha-1 microglobulinKIM-1 osteopontin alpha-1 microglobulin KIM-1 THP alpha-1 microglobulinKIM-1 TIMP-1 alpha-1 microglobulin KIM-1 TFF-3 alpha-1 microglobulinKIM-1 VEGF alpha-1 microglobulin microalbumin NGAL alpha-1 microglobulinmicroalbumin osteopontin alpha-1 microglobulin microalbumin THP alpha-1microglobulin microalbumin TIMP-1 alpha-1 microglobulin microalbuminTFF-3 alpha-1 microglobulin microalbumin VEGF alpha-1 microglobulin NGALosteopontin alpha-1 microglobulin NGAL THP alpha-1 microglobulin NGALTIMP-1 alpha-1 microglobulin NGAL TFF-3 alpha-1 microglobulin NGAL VEGFalpha-1 microglobulin osteopontin THP alpha-1 microglobulin osteopontinTIMP-1 alpha-1 microglobulin osteopontin TFF-3 alpha-1 microglobulinosteopontin VEGF alpha-1 microglobulin THP TIMP-1 alpha-1 microglobulinTHP TFF-3 alpha-1 microglobulin THP VEGF alpha-1 microglobulin TIMP-1TFF-3 alpha-1 microglobulin TIMP-1 VEGF alpha-1 microglobulin TFF-3 VEGFbeta-2 microglobulin calbindin clusterin beta-2 microglobulin calbindinCTGF beta-2 microglobulin calbindin creatinine beta-2 microglobulincalbindin cystatin C beta-2 microglobulin calbindin GST-alpha beta-2microglobulin calbindin KIM-1 beta-2 microglobulin calbindinmicroalbumin beta-2 microglobulin calbindin NGAL beta-2 microglobulincalbindin osteopontin beta-2 microglobulin calbindin THP beta-2microglobulin calbindin TIMP-1 beta-2 microglobulin calbindin TFF-3beta-2 microglobulin calbindin VEGF beta-2 microglobulin clusterin CTGFbeta-2 microglobulin clusterin creatinine beta-2 microglobulin clusterincystatin C beta-2 microglobulin clusterin GST-alpha beta-2 microglobulinclusterin KIM-1 beta-2 microglobulin clusterin microalbumin beta-2microglobulin clusterin NGAL beta-2 microglobulin clusterin osteopontinbeta-2 microglobulin clusterin THP beta-2 microglobulin clusterin TIMP-1beta-2 microglobulin clusterin TFF-3 beta-2 microglobulin clusterin VEGFbeta-2 microglobulin CTGF creatinine beta-2 microglobulin CTGF cystatinC beta-2 microglobulin CTGF GST-alpha beta-2 microglobulin CTGF KIM-1beta-2 microglobulin CTGF microalbumin beta-2 microglobulin CTGF NGALbeta-2 microglobulin CTGF osteopontin beta-2 microglobulin CTGF THPbeta-2 microglobulin CTGF TIMP-1 beta-2 microglobulin CTGF TFF-3 beta-2microglobulin CTGF VEGF beta-2 microglobulin creatinine cystatin Cbeta-2 microglobulin creatinine GST-alpha beta-2 microglobulincreatinine KIM-1 beta-2 microglobulin creatinine microalbumin beta-2microglobulin creatinine NGAL beta-2 microglobulin creatinineosteopontin beta-2 microglobulin creatinine THP beta-2 microglobulincreatinine TIMP-1 beta-2 microglobulin creatinine TFF-3 beta-2microglobulin creatinine VEGF beta-2 microglobulin cystatin C GST-alphabeta-2 microglobulin cystatin C KIM-1 beta-2 microglobulin cystatin Cmicroalbumin beta-2 microglobulin cystatin C NGAL beta-2 microglobulincystatin C osteopontin beta-2 microglobulin cystatin C THP beta-2microglobulin cystatin C TIMP-1 beta-2 microglobulin cystatin C TFF-3beta-2 microglobulin cystatin C VEGF beta-2 microglobulin GST-alphaKIM-1 beta-2 microglobulin GST-alpha microalbumin beta-2 microglobulinGST-alpha NGAL beta-2 microglobulin GST-alpha osteopontin beta-2microglobulin GST-alpha THP beta-2 microglobulin GST-alpha TIMP-1 beta-2microglobulin GST-alpha TFF-3 beta-2 microglobulin GST-alpha VEGF beta-2microglobulin KIM-1 microalbumin beta-2 microglobulin KIM-1 NGAL beta-2microglobulin KIM-1 osteopontin beta-2 microglobulin KIM-1 THP beta-2microglobulin KIM-1 TIMP-1 beta-2 microglobulin KIM-1 TFF-3 beta-2microglobulin KIM-1 VEGF beta-2 microglobulin microalbumin NGAL beta-2microglobulin microalbumin osteopontin beta-2 microglobulin microalbuminTHP beta-2 microglobulin microalbumin TIMP-1 beta-2 microglobulinmicroalbumin TFF-3 beta-2 microglobulin microalbumin VEGF beta-2microglobulin NGAL osteopontin beta-2 microglobulin NGAL THP beta-2microglobulin NGAL TIMP-1 beta-2 microglobulin NGAL TFF-3 beta-2microglobulin NGAL VEGF beta-2 microglobulin osteopontin THP beta-2microglobulin osteopontin TIMP-1 beta-2 microglobulin osteopontin TFF-3beta-2 microglobulin osteopontin VEGF beta-2 microglobulin THP TIMP-1beta-2 microglobulin THP TFF-3 beta-2 microglobulin THP VEGF beta-2microglobulin TIMP-1 TFF-3 beta-2 microglobulin TIMP-2 VEGF beta-2microglobulin TFF-3 VEGF calbindin clusterin CTGF calbindin clusterincreatinine calbindin clusterin cystatin C calbindin clusterin GST-alphacalbindin clusterin KIM-1 calbindin clusterin microalbumin calbindinclusterin NGAL calbindin clusterin osteopontin calbindin clusterin THPcalbindin clusterin TIMP-1 calbindin clusterin TFF-3 calbindin clusterinVEGF calbindin CTGF creatinine calbindin CTGF cystatin C calbindin CTGFGST-alpha calbindin CTGF KIM-1 calbindin CTGF microalbumin calbindinCTGF NGAL calbindin CTGF osteopontin calbindin CTGF THP calbindin CTGFTIMP-1 calbindin CTGF TFF-3 calbindin CTGF VEGF calbindin creatininecystatin C calbindin creatinine GST-alpha calbindin creatinine KIM-1calbindin creatinine microalbumin calbindin creatinine NGAL calbindincreatinine osteopontin calbindin creatinine THP calbindin creatinineTIMP-1 calbindin creatinine TFF-3 calbindin creatinine VEGF calbindincystatin C GST-alpha calbindin cystatin C KIM-1 calbindin cystatin Cmicroalbumin calbindin cystatin C NGAL calbindin cystatin C osteopontincalbindin cystatin C THP calbindin cystatin C TIMP-1 calbindin cystatinC TFF-3 calbindin cystatin C VEGF calbindin GST-alpha KIM-1 calbindinGST-alpha microalbumin calbindin GST-alpha NGAL calbindin GST-alphaosteopontin calbindin GST-alpha THP calbindin GST-alpha TIMP-1 calbindinGST-alpha TFF-3 calbindin GST-alpha VEGF calbindin KIM-1 microalbumincalbindin KIM-1 NGAL calbindin KIM-1 osteopontin calbindin KIM-1 THPcalbindin KIM-1 TIMP-1 calbindin KIM-1 TFF-3 calbindin KIM-1 VEGFcalbindin microalbumin NGAL calbindin microalbumin osteopontin calbindinmicroalbumin THP calbindin microalbumin TIMP-1 calbindin microalbuminTFF-3 calbindin microalbumin VEGF calbindin NGAL osteopontin calbindinNGAL THP calbindin NGAL TIMP-1 calbindin NGAL TFF-3 calbindin NGAL VEGFcalbindin osteopontin THP calbindin osteopontin TIMP-1 calbindinosteopontin TFF-3 calbindin osteopontin VEGF calbindin THP TIMP-1calbindin THP TFF-3 calbindin THP VEGF calbindin TIMP-1 TFF-3 calbindinTIMP-1 VEGF calbindin TFF-3 VEGF clusterin CTGF creatinine clusterinCTGF cystatin C clusterin CTGF GST-alpha clusterin CTGF KIM-1 clusterinCTGF microalbumin clusterin CTGF NGAL clusterin CTGF osteopontinclusterin CTGF THP clusterin CTGF TIMP-1 clusterin CTGF TFF-3 clusterinCTGF VEGF clusterin creatinine cystatin C clusterin creatinine GST-alphaclusterin creatinine KIM-1 clusterin creatinine microalbumin clusterincreatinine NGAL clusterin creatinine osteopontin clusterin creatinineTHP clusterin creatinine TIMP-1 clusterin creatinine TFF-3 clusterincreatinine VEGF clusterin cystatin C GST-alpha clusterin cystatin CKIM-1 clusterin cystatin C microalbumin clusterin cystatin C NGALclusterin cystatin C osteopontin clusterin cystatin C THP clusterincystatin C TIMP-1 clusterin cystatin C TFF-3 clusterin cystatin C VEGFclusterin GST-alpha KIM-1 clusterin GST-alpha microalbumin clusterinGST-alpha NGAL clusterin GST-alpha osteopontin clusterin GST-alpha THPclusterin GST-alpha TIMP-1 clusterin GST-alpha TFF-3 clusterin GST-alphaVEGF clusterin KIM-1 microalbumin clusterin KIM-1 NGAL clusterin KIM-1osteopontin clusterin KIM-1 THP clusterin KIM-1 TIMP-1 clusterin KIM-1TFF-3 clusterin KIM-1 VEGF clusterin microalbumin NGAL clusterinmicroalbumin osteopontin clusterin microalbumin THP clusterinmicroalbumin TIMP-1 clusterin microalbumin TFF-3 clusterin microalbuminVEGF clusterin NGAL osteopontin clusterin NGAL THP clusterin NGAL TIMP-1clusterin NGAL TFF-3 clusterin NGAL VEGF clusterin osteopontin THPclusterin osteopontin TIMP-1 clusterin osteopontin TFF-3 clusterinosteopontin VEGF clusterin THP TIMP-1 clusterin THP TFF-3 clusterin THPVEGF clusterin TIMP-1 TFF-3 clusterin TIMP-1 VEGF clusterin TFF-3 VEGFCTGF creatinine cystatin C CTGF creatinine GST-alpha CTGF creatinineKIM-1 CTGF creatinine microalbumin CTGF creatinine NGAL CTGF creatinineosteopontin CTGF creatinine THP CTGF creatinine TIMP-1 CTGF creatinineTFF-3 CTGF creatinine VEGF CTGF cystatin C GST-alpha CTGF cystatin CKIM-1 CTGF cystatin C microalbumin CTGF cystatin C NGAL CTGF cystatin Costeopontin CTGF cystatin C THP CTGF cystatin C TIMP-1 CTGF cystatin CTFF-3 CTGF cystatin C VEGF CTGF GST-alpha KIM-1 CTGF GST-alphamicroalbumin CTGF GST-alpha NGAL CTGF GST-alpha osteopontin CTGFGST-alpha THP CTGF GST-alpha TIMP-1 CTGF GST-alpha TFF-3 CTGF GST-alphaVEGF CTGF KIM-1 microalbumin CTGF KIM-1 NGAL CTGF KIM-1 osteopontin CTGFKIM-1 THP CTGF KIM-1 TIMP-1 CTGF KIM-1 TFF-3 CTGF KIM-1 VEGF CTGFmicroalbumin NGAL CTGF microalbumin osteopontin CTGF microalbumin THPCTGF microalbumin TIMP-1 CTGF microalbumin TFF-3 CTGF microalbumin VEGFCTGF NGAL osteopontin CTGF NGAL THP CTGF NGAL TIMP-1 CTGF NGAL TFF-3CTGF NGAL VEGF CTGF osteopontin THP CTGF osteopontin TIMP-1 CTGFosteopontin TFF-3 CTGF osteopontin VEGF CTGF THP TIMP-1 CTGF THP TFF-3CTGF THP VEGF CTGF TIMP-1 TFF-3 CTGF TIMP-1 VEGF CTGF TFF-3 VEGFcreatinine cystatin C GST-alpha creatinine cystatin C KIM-1 creatininecystatin C microalbumin creatinine cystatin C NGAL creatinine cystatin Costeopontin creatinine cystatin C THP creatinine cystatin C TIMP-1creatinine cystatin C TFF-3 creatinine cystatin C VEGF creatinineGST-alpha KIM-1 creatinine GST-alpha microalbumin creatinine GST-alphaNGAL creatinine GST-alpha osteopontin creatinine GST-alpha THPcreatinine GST-alpha TIMP-1 creatinine GST-alpha TFF-3 creatinineGST-alpha VEGF creatinine KIM-1 microalbumin creatinine KIM-1 NGALcreatinine KIM-1 osteopontin creatinine KIM-1 THP creatinine KIM-1TIMP-1 creatinine KIM-1 TFF-3 creatinine KIM-1 VEGF creatininemicroalbumin NGAL creatinine microalbumin osteopontin creatininemicroalbumin THP creatinine microalbumin TIMP-1 creatinine microalbuminTFF-3 creatinine microalbumin VEGF creatinine NGAL osteopontincreatinine NGAL THP creatinine NGAL TIMP-1 creatinine NGAL TFF-3creatinine NGAL VEGF creatinine osteopontin THP creatinine osteopontinTIMP-1 creatinine osteopontin TFF-3 creatinine osteopontin VEGFcreatinine THP TIMP-1 creatinine THP TFF-3 creatinine THP VEGFcreatinine TIMP-1 TFF-3 creatinine TIMP-1 VEGF creatinine TFF-3 VEGFcystatin C GST-alpha KIM-1 cystatin C GST-alpha microalbumin cystatin CGST-alpha NGAL cystatin C GST-alpha osteopontin cystatin C GST-alpha THPcystatin C GST-alpha TIMP-1 cystatin C GST-alpha TFF-3 cystatin CGST-alpha VEGF cystatin C KIM-1 microalbumin cystatin C KIM-1 NGALcystatin C KIM-1 osteopontin cystatin C KIM-1 THP cystatin C KIM-1TIMP-1 cystatin C KIM-1 TFF-3 cystatin C KIM-1 VEGF cystatin Cmicroalbumin NGAL cystatin C microalbumin osteopontin cystatin Cmicroalbumin THP cystatin C microalbumin TIMP-1 cystatin C microalbuminTFF-3 cystatin C microalbumin VEGF cystatin C NGAL osteopontin cystatinC NGAL THP cystatin C NGAL TIMP-1 cystatin C NGAL TFF-3 cystatin C NGALVEGF cystatin C osteopontin THP cystatin C osteopontin TIMP-1 cystatin Costeopontin TFF-3 cystatin C osteopontin VEGF cystatin C THP TIMP-1cystatin C THP TFF-3 cystatin C THP VEGF cystatin C TIMP-1 TFF-3cystatin C TIMP-1 VEGF cystatin C TFF-3 VEGF GST-alpha KIM-1microalbumin GST-alpha KIM-1 NGAL GST-alpha KIM-1 osteopontin GST-alphaKIM-1 THP GST-alpha KIM-1 TIMP-1 GST-alpha KIM-1 TFF-3 GST-alpha KIM-1VEGF GST-alpha microalbumin NGAL GST-alpha microalbumin osteopontinGST-alpha microalbumin THP GST-alpha microalbumin TIMP-1 GST-alphamicroalbumin TFF-3 GST-alpha microalbumin VEGF GST-alpha NGALosteopontin GST-alpha NGAL THP GST-alpha NGAL TIMP-1 GST-alpha NGALTFF-3 GST-alpha NGAL VEGF GST-alpha osteopontin THP GST-alphaosteopontin TIMP-1 GST-alpha osteopontin TFF-3 GST-alpha osteopontinVEGF GST-alpha THP TIMP-1 GST-alpha THP TFF-3 GST-alpha THP VEGFGST-alpha TIMP-1 TFF-3 GST-alpha TIMP-1 VEGF GST-alpha TFF-3 VEGF KIM-1microalbumin NGAL KIM-1 microalbumin osteopontin KIM-1 microalbumin THPKIM-1 microalbumin TIMP-1 KIM-1 microalbumin TFF-3 KIM-1 microalbuminVEGF KIM-1 NGAL osteopontin KIM-1 NGAL THP KIM-1 NGAL TIMP-1 KIM-1 NGALTFF-3 KIM-1 NGAL VEGF KIM-1 osteopontin THP KIM-1 osteopontin TIMP-1KIM-1 osteopontin TFF-3 KIM-1 osteopontin VEGF KIM-1 THP TIMP-1 KIM-1THP TFF-3 KIM-1 THP VEGF KIM-1 TIMP-1 TFF-3 KIM-1 TIMP-1 VEGF KIM-1TFF-3 VEGF microalbumin NGAL osteopontin microalbumin NGAL THPmicroalbumin NGAL TIMP-1 microalbumin NGAL TFF-3 microalbumin NGAL VEGFmicroalbumin osteopontin THP microalbumin osteopontin TIMP-1microalbumin osteopontin TFF-3 microalbumin osteopontin VEGFmicroalbumin THP TIMP-1 microalbumin THP TFF-3 microalbumin THP VEGFmicroalbumin TIMP-1 TFF-3 microalbumin TIMP-1 VEGF microalbumin TFF-3VEGF NGAL osteopontin THP NGAL osteopontin TIMP-1 NGAL osteopontin TFF-3NGAL osteopontin VEGF NGAL THP TIMP-1 NGAL THP TFF-3 NGAL THP VEGF NGALTIMP-1 TFF-3 NGAL TIMP-1 VEGF NGAL TFF-3 VEGF osteopontin THP TIMP-1osteopontin THP TFF-3 osteopontin THP VEGF osteopontin TIMP-1 TFF-3osteopontin TIMP-1 VEGF osteopontin TFF-3 VEGF THP TIMP-1 TFF-3 THPTIMP-1 VEGF THP TFF-3 VEGF TIMP-1 TFF-3 VEGF

In one exemplary embodiment, the combination of sample analytes mayinclude creatinine, KIM-1, and THP. In another exemplary embodiment, thecombination of sample analytes may include microalbumin, creatinine, andKIM-1. In yet another exemplary embodiment, the combination of sampleanalytes may include KIM-1, THP, and B2M. In still another exemplaryembodiment, the combination of sample analytes may include microalbumin,A1M, and creatinine. In an alternative exemplary embodiment, the sampleis a urine sample, and the combination of sample analytes may includemicroalbumin, alpha-1 microglobulin, NGAL, KIM-1, THP, and clusterin. Inanother alternative exemplary embodiment, the sample is a plasma sample,and the combination of sample analytes may include alpha-1microglobulin, cystatin C, THP, beta-2 microglobulin, TIMP-1, and KIM-1.

III. Test Sample

The method for diagnosing, monitoring, or determining a renal disorderinvolves determining the presence of sample analytes in a test sample. Atest sample, as defined herein, is an amount of bodily fluid taken froma mammal. Non-limiting examples of bodily fluids include urine, blood,plasma, serum, saliva, semen, perspiration, tears, mucus, and tissuelysates. In an exemplary embodiment, the bodily fluid contained in thetest sample is urine, plasma, or serum.

(a) Mammals

A mammal, as defined herein, is any organism that is a member of theclass Mammalia. Non-limiting examples of mammals appropriate for thevarious embodiments may include humans, apes, monkeys, rats, mice, dogs,cats, pigs, and livestock including cattle and oxen. In an exemplaryembodiment, the mammal is a human.

(b) Devices and Methods of Taking Bodily Fluids from Mammals

The bodily fluids of the test sample may be taken from the mammal usingany known device or method so long as the analytes to be measured by themultiplexed assay are not rendered undetectable by the multiplexedassay. Non-limiting examples of devices or methods suitable for takingbodily fluid from a mammal include urine sample cups, urethralcatheters, swabs, hypodermic needles, thin needle biopsies, hollowneedle biopsies, punch biopsies, metabolic cages, and aspiration.

In order to adjust the expected concentrations of the sample analytes inthe test sample to fall within the dynamic range of the multiplexedassay, the test sample may be diluted to reduce the concentration of thesample analytes prior to analysis. The degree of dilution may depend ona variety of factors including but not limited to the type ofmultiplexed assay used to measure the analytes, the reagents utilized inthe multiplexed assay, and the type of bodily fluid contained in thetest sample. In one embodiment, the test sample is diluted by adding avolume of diluent ranging from about ½ of the original test samplevolume to about 50,000 times the original test sample volume.

In one exemplary embodiment, if the test sample is human urine and themultiplexed assay is an antibody-based capture-sandwich assay, the testsample is diluted by adding a volume of diluent that is about 100 timesthe original test sample volume prior to analysis. In another exemplaryembodiment, if the test sample is human serum and the multiplexed assayis an antibody-based capture-sandwich assay, the test sample is dilutedby adding a volume of diluent that is about 5 times the original testsample volume prior to analysis. In yet another exemplary embodiment, ifthe test sample is human plasma and the multiplexed assay is anantibody-based capture-sandwich assay, the test sample is diluted byadding a volume of diluent that is about 2,000 times the original testsample volume prior to analysis.

The diluent may be any fluid that does not interfere with the functionof the multiplexed assay used to measure the concentration of theanalytes in the test sample. Non-limiting examples of suitable diluentsinclude deionized water, distilled water, saline solution, Ringer'ssolution, phosphate buffered saline solution, TRIS-buffered salinesolution, standard saline citrate, and HEPES-buffered saline.

IV. Multiplexed Assay Device

In one embodiment, the concentration of a combination of sample analytesis measured using a multiplexed assay device capable of measuring theconcentrations of up to sixteen of the biomarker analytes. A multiplexedassay device, as defined herein, is an assay capable of simultaneouslydetermining the concentration of three or more different sample analytesusing a single device and/or method. Any known method of measuring theconcentration of the biomarker analytes may be used for the multiplexedassay device. Non-limiting examples of measurement methods suitable forthe multiplexed assay device may include electrophoresis, massspectrometry, protein microarrays, surface plasmon resonance andimmunoassays including but not limited to western blot,immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA)methods, and particle-based capture-sandwich immunoassays.

(a) Multiplexed Immunoassay Device

In one embodiment, the concentrations of the analytes in the test sampleare measured using a multiplexed immunoassay device that utilizescapture antibodies marked with indicators to determine the concentrationof the sample analytes.

(i) Capture Antibodies

In the same embodiment, the multiplexed immunoassay device includesthree or more capture antibodies. Capture antibodies, as defined herein,are antibodies in which the antigenic determinant is one of thebiomarker analytes. Each of the at least three capture antibodies has aunique antigenic determinant that is one of the biomarker analytes. Whencontacted with the test sample, the capture antibodies formantigen-antibody complexes in which the analytes serve as antigens.

The term “antibody,” as used herein, encompasses a monoclonal ab, anantibody fragment, a chimeric antibody, and a single-chain antibody.

In some embodiments, the capture antibodies may be attached to asubstrate in order to immobilize any analytes captured by the captureantibodies. Non-limiting examples of suitable substrates include paper,cellulose, glass, or plastic strips, beads, or surfaces, such as theinner surface of the well of a microtitration tray. Suitable beads mayinclude polystyrene or latex microspheres.

(ii) Indicators

In one embodiment of the multiplexed immunoassay device, an indicator isattached to each of the three or more capture antibodies. The indicator,as defined herein, is any compound that registers a measurable change toindicate the presence of one of the sample analytes when bound to one ofthe capture antibodies. Non-limiting examples of indicators includevisual indicators and electrochemical indicators.

Visual indicators, as defined herein, are compounds that register achange by reflecting a limited subset of the wavelengths of lightilluminating the indicator, by fluorescing light after beingilluminated, or by emitting light via chemiluminescence. The changeregistered by visual indicators may be in the visible light spectrum, inthe infrared spectrum, or in the ultraviolet spectrum. Non-limitingexamples of visual indicators suitable for the multiplexed immunoassaydevice include nanoparticulate gold, organic particles such aspolyurethane or latex microspheres loaded with dye compounds, carbonblack, fluorophores, phycoerythrin, radioactive isotopes, nanoparticles,quantum dots, and enzymes such as horseradish peroxidase or alkalinephosphatase that react with a chemical substrate to form a colored orchemiluminescent product.

Electrochemical indicators, as defined herein, are compounds thatregister a change by altering an electrical property. The changesregistered by electrochemical indicators may be an alteration inconductivity, resistance, capacitance, current conducted in response toan applied voltage, or voltage required to achieve a desired current.Non-limiting examples of electrochemical indicators include redoxspecies such as ascorbate (vitamin C), vitamin E, glutathione,polyphenols, catechols, quercetin, phytoestrogens, penicillin,carbazole, murranes, phenols, carbonyls, benzoates, and trace metal ionssuch as nickel, copper, cadmium, iron and mercury.

In this same embodiment, the test sample containing a combination ofthree or more sample analytes is contacted with the capture antibodiesand allowed to form antigen-antibody complexes in which the sampleanalytes serve as the antigens. After removing any uncomplexed captureantibodies, the concentrations of the three or more analytes aredetermined by measuring the change registered by the indicators attachedto the capture antibodies.

In one exemplary embodiment, the indicators are polyurethane or latexmicrospheres loaded with dye compounds and phycoerythrin.

(b) Multiplexed Sandwich Immunoassay Device

In another embodiment, the multiplexed immunoassay device has a sandwichassay format. In this embodiment, the multiplexed sandwich immunoassaydevice includes three or more capture antibodies as previouslydescribed. However, in this embodiment, each of the capture antibodiesis attached to a capture agent that includes an antigenic moiety. Theantigenic moiety serves as the antigenic determinant of a detectionantibody, also included in the multiplexed immunoassay device of thisembodiment. In addition, an indicator is attached to the detectionantibody.

In this same embodiment, the test sample is contacted with the captureantibodies and allowed to form antigen-antibody complexes in which thesample analytes serve as antigens. The detection antibodies are thencontacted with the test sample and allowed to form antigen-antibodycomplexes in which the capture agent serves as the antigen for thedetection antibody. After removing any uncomplexed detection antibodiesthe concentration of the analytes are determined by measuring thechanges registered by the indicators attached to the detectionantibodies.

(c) Multiplexing Approaches

In the various embodiments of the multiplexed immunoassay devices, theconcentrations of each of the sample analytes may be determined usingany approach known in the art. In one embodiment, a single indicatorcompound is attached to each of the three or more antibodies. Inaddition, each of the capture antibodies having one of the sampleanalytes as an antigenic determinant is physically separated into adistinct region so that the concentration of each of the sample analytesmay be determined by measuring the changes registered by the indicatorsin each physically separate region corresponding to each of the sampleanalytes.

In another embodiment, each antibody having one of the sample analytesas an antigenic determinant is marked with a unique indicator. In thismanner, a unique indicator is attached to each antibody having a singlesample analyte as its antigenic determinant. In this embodiment, allantibodies may occupy the same physical space. The concentration of eachsample analyte is determined by measuring the change registered by theunique indicator attached to the antibody having the sample analyte asan antigenic determinant.

(d) Microsphere-Based Capture-Sandwich Immunoassay Device

In an exemplary embodiment, the multiplexed immunoassay device is amicrosphere-based capture-sandwich immunoassay device. In thisembodiment, the device includes a mixture of three or morecapture-antibody microspheres, in which each capture-antibodymicrosphere corresponds to one of the biomarker analytes. Eachcapture-antibody microsphere includes a plurality of capture antibodiesattached to the outer surface of the microsphere. In this sameembodiment, the antigenic determinant of all of the capture antibodiesattached to one microsphere is the same biomarker analyte.

In this embodiment of the device, the microsphere is a small polystyreneor latex sphere that is loaded with an indicator that is a dye compound.The microsphere may be between about 3 μm and about 5 μm in diameter.Each capture-antibody microsphere corresponding to one of the biomarkeranalytes is loaded with the same indicator. In this manner, eachcapture-antibody microsphere corresponding to a biomarker analyte isuniquely color-coded.

In this same exemplary embodiment, the multiplexed immunoassay devicefurther includes three or more biotinylated detection antibodies inwhich the antigenic determinant of each biotinylated detection antibodyis one of the biomarker analytes. The device further includes aplurality of streptaviden proteins complexed with a reporter compound. Areporter compound, as defined herein, is an indicator selected toregister a change that is distinguishable from the indicators used tomark the capture-antibody microspheres.

The concentrations of the sample analytes may be determined bycontacting the test sample with a mixture of capture-antigenmicrospheres corresponding to each sample analyte to be measured. Thesample analytes are allowed to form antigen-antibody complexes in whicha sample analyte serves as an antigen and a capture antibody attached tothe microsphere serves as an antibody. In this manner, the sampleanalytes are immobilized onto the capture-antigen microspheres. Thebiotinylated detection antibodies are then added to the test sample andallowed to form antigen-antibody complexes in which the analyte servesas the antigen and the biotinylated detection antibody serves as theantibody. The streptaviden-reporter complex is then added to the testsample and allowed to bind to the biotin moieties of the biotinylateddetection antibodies. The antigen-capture microspheres may then berinsed and filtered.

In this embodiment, the concentration of each analyte is determined byfirst measuring the change registered by the indicator compound embeddedin the capture-antigen microsphere in order to identify the particularanalyte. For each microsphere corresponding to one of the biomarkeranalytes, the quantity of analyte immobilized on the microsphere isdetermined by measuring the change registered by the reporter compoundattached to the microsphere.

For example, the indicator embedded in the microspheres associated withone sample analyte may register an emission of orange light, and thereporter may register an emission of green light. In this example, adetector device may measure the intensity of orange light and greenlight separately. The measured intensity of the green light woulddetermine the concentration of the analyte captured on the microsphere,and the intensity of the orange light would determine the specificanalyte captured on the microsphere.

Any sensor device may be used to detect the changes registered by theindicators embedded in the microspheres and the changes registered bythe reporter compound, so long as the sensor device is sufficientlysensitive to the changes registered by both indicator and reportercompound. Non-limiting examples of suitable sensor devices includespectrophotometers, photosensors, colorimeters, cyclic coulometrydevices, and flow cytometers. In an exemplary embodiment, the sensordevice is a flow cytometer.

V. Method for Diagnosing, Monitoring, or Determining a Renal Disorder

In one embodiment, a method is provided for diagnosing, monitoring, ordetermining diabetic nephropathy or an associated disorder that includesproviding a test sample, determining the concentration of a combinationof three or more sample analytes, comparing the measured concentrationsof the combination of sample analytes to the entries of a dataset, andidentifying diabetic nephropathy or an associated disorder based on thecomparison between the concentrations of the sample analytes and theminimum diagnostic concentrations contained within each entry of thedataset.

(a) Diagnostic Dataset

In an embodiment, the concentrations of the sample analytes are comparedto the entries of a dataset. In this embodiment, each entry of thedataset includes a combination of three or more minimum diagnosticconcentrations indicative of a particular renal disorder. A minimumdiagnostic concentration, as defined herein, is the concentration of ananalyte that defines the limit between the concentration rangecorresponding to normal, healthy renal function and the concentrationreflective of a particular renal disorder. In one embodiment, eachminimum diagnostic concentration is the maximum concentration of therange of analyte concentrations for a healthy, normal individual. Theminimum diagnostic concentration of an analyte depends on a number offactors including but not limited to the particular analyte and the typeof bodily fluid contained in the test sample. As an illustrativeexample, Table 1 lists the expected normal ranges of the biomarkeranalytes in human plasma, serum, and urine.

TABLE 1 Normal Concentration Ranges In Human Plasma, Serum, and UrineSamples Plasma Sera Urine Analyte Units low high low high low highCalbindin ng/ml — <5.0 — <2.6 4.2 233 Clusterin μg/ml 86 134 37 152 —<0.089 CTGF ng/ml 2.8 7.5 — <8.2 — <0.90 GST-alpha ng/ml 6.7 62 1.2 52 —<26 KIM-1 ng/ml 0.053 0.57 — <0.35 0.023 0.67 VEGF pg/ml 222 855 2191630 69 517 B2M μg/ml 0.68 2.2 1.00 2.6 <0.17 Cyst C ng/ml 608 1170 4761250 3.9 79 NGAL ng/ml 89 375 102 822 2.9 81 OPN ng/ml 4.1 25 0.49 12291 6130 TIMP-1 ng/ml 50 131 100 246 — <3.9 A1M μg/ml 6.2 16 5.7 17 —<4.2 THP μg/ml 0.0084 0.052 0.0079 0.053 0.39 2.6 TFF3 μg/ml 0.040 0.490.021 0.17 — <21 Creatinine mg/dL — — — — 13 212 Microalbumin μg/ml — —— — — >16

In one embodiment, the high values shown for each of the biomarkeranalytes in Table 1 for the analytic concentrations in human plasma,sera and urine are the minimum diagnostics values for the analytes inhuman plasma, sera, and urine, respectively. In one exemplaryembodiment, the minimum diagnostic concentration in human plasma ofalpha-1 microglobulin is about 16 μg/ml, beta-2 microglobulin is about2.2 μg/ml, calbindin is greater than about 5 ng/ml, clusterin is about134 μg/ml, CTGF is about 16 ng/ml, cystatin C is about 1170 ng/ml,GST-alpha is about 62 ng/ml, KIM-1 is about 0.57 ng/ml, NGAL is about375 ng/ml, osteopontin is about 25 ng/ml, THP is about 0.052 μg/ml,TIMP-1 is about 131 ng/ml, TFF-3 is about 0.49 μg/ml, and VEGF is about855 μg/ml.

In another exemplary embodiment, the minimum diagnostic concentration inhuman sera of alpha-1 microglobulin is about 17 μg/ml, beta-2microglobulin is about 2.6 μg/ml, calbindin is greater than about 2.6ng/ml, clusterin is about 152 μg/ml, CTGF is greater than about 8.2ng/ml, cystatin C is about 1250 ng/ml, GST-alpha is about 52 ng/ml,KIM-1 is greater than about 0.35 ng/ml, NGAL is about 822 ng/ml,osteopontin is about 12 ng/ml, THP is about 0.053 μg/ml, TIMP-1 is about246 ng/ml, TFF-3 is about 0.17 μg/ml, and VEGF is about 1630 μg/ml.

In yet another exemplary embodiment, the minimum diagnosticconcentration in human urine of alpha-1 microglobulin is about 233μg/ml, beta-2 microglobulin is greater than about 0.17 μg/ml, calbindinis about 233 ng/ml, clusterin is greater than about 0.089 μg/ml, CTGF isgreater than about 0.90 ng/ml, cystatin C is about 1170 ng/ml, GST-alphais greater than about 26 ng/ml, KIM-1 is about 0.67 ng/ml, NGAL is about81 ng/ml, osteopontin is about 6130 ng/ml, THP is about 2.6 μg/ml,TIMP-1 is greater than about 3.9 ng/ml, TFF-3 is greater than about 21μg/ml, and VEGF is about 517 μg/ml.

In one embodiment, the minimum diagnostic concentrations represent themaximum level of analyte concentrations falling within an expectednormal range. Diabetic nephropathy or an associated disorder may beindicated if the concentration of an analyte is higher than the minimumdiagnostic concentration for the analyte.

If diminished concentrations of a particular analyte are known to beassociated with diabetic nephropathy or an associated disorder, theminimum diagnostic concentration may not be an appropriate diagnosticcriterion for identifying diabetic nephropathy or an associated disorderindicated by the sample analyte concentrations. In these cases, amaximum diagnostic concentration may define the limit between theexpected normal concentration range for the analyte and a sampleconcentration reflective of diabetic nephropathy or an associateddisorder. In those cases in which a maximum diagnostic concentration isthe appropriate diagnostic criterion, sample concentrations that fallbelow a maximum diagnostic concentration may indicate diabeticnephropathy or an associated disorder.

A critical feature of the method of the multiplexed analyte panel isthat a combination of sample analyte concentrations may be used todiagnose diabetic nephropathy or an associated disorder. In addition tocomparing subsets of the biomarker analyte concentrations to diagnosticcriteria, the analytes may be algebraically combined and compared tocorresponding diagnostic criteria. In one embodiment, two or more sampleanalyte concentrations may be added and/or subtracted to determine acombined analyte concentration. In another embodiment, two or moresample analyte concentrations may be multiplied and/or divided todetermine a combined analyte concentration. To identify diabeticnephropathy or an associated disorder, the combined analyteconcentration may be compared to a diagnostic criterion in which thecorresponding minimum or maximum diagnostic concentrations are combinedusing the same algebraic operations used to determine the combinedanalyte concentration.

In yet another embodiment, the analyte concentration measured from atest sample containing one type of body fluid may be algebraicallycombined with an analyte concentration measured from a second testsample containing a second type of body fluid to determine a combinedanalyte concentration. For example, the ratio of urine calbindin toplasma calbindin may be determined and compared to a correspondingminimum diagnostic urine: plasma calbindin ratio to identify aparticular renal disorder.

A variety of methods known in the art may be used to define thediagnostic criteria used to identify diabetic nephropathy or anassociated disorder. In one embodiment, any sample concentration fallingoutside the expected normal range indicates diabetic nephropathy or anassociated disorder. In another embodiment, the multiplexed analytepanel may be used to evaluate the analyte concentrations in test samplestaken from a population of patients having diabetic nephropathy or anassociated disorder and compared to the normal expected analyteconcentration ranges. In this same embodiment, any sample analyteconcentrations that are significantly higher or lower than the expectednormal concentration range may be used to define a minimum or maximumdiagnostic concentration, respectively. A number of studies comparingthe biomarker concentration ranges of a population of patients having arenal disorder to the corresponding analyte concentrations from apopulation of normal healthy subjects are described in the examplessection below.

In an exemplary embodiment, an analyte value in a test sample higherthan the minimum diagnostic value for the top 3 analytes of theparticular sample type (e.g. plasma, urine, etc.), wherein the top 3 aredetermined by the random forest classification method may result in adiagnosis of diabetic nephropathy.

VI. Automated Method for Diagnosing, Monitoring, or Determining a RenalDisorder

In one embodiment, a system for diagnosing, monitoring, or determiningdiabetic nephropathy or an associated disorder in a mammal is providedthat includes a database to store a plurality of renal disorder databaseentries, and a processing device that includes the modules of a renaldisorder determining application. In this embodiment, the modules areexecutable by the processing device, and include an analyte inputmodule, a comparison module, and an analysis module.

The analyte input module receives three or more sample analyteconcentrations that include the biomarker analytes. In one embodiment,the sample analyte concentrations are entered as input by a user of theapplication. In another embodiment, the sample analyte concentrationsare transmitted directly to the analyte input module by the sensordevice used to measure the sample analyte concentration via a datacable, infrared signal, wireless connection or other methods of datatransmission known in the art.

The comparison module compares each sample analyte concentration to anentry of a renal disorder database. Each entry of the renal disorderdatabase includes a list of minimum diagnostic concentrations reflectiveof a particular renal disorder. The entries of the renal disorderdatabase may further contain additional minimum diagnosticconcentrations to further define diagnostic criteria including but notlimited to minimum diagnostic concentrations for additional types ofbodily fluids, additional types of mammals, and severities of aparticular disorder.

The analysis module determines a most likely renal disorder by combiningthe particular renal disorders identified by the comparison module forall of the sample analyte concentrations. In one embodiment, the mostlikely renal disorder is the particular renal disorder from the databaseentry having the most minimum diagnostic concentrations that are lessthan the corresponding sample analyte concentrations. In anotherembodiment, the most likely renal disorder is the particular renaldisorder from the database entry having minimum diagnosticconcentrations that are all less than the corresponding sample analyteconcentrations. In yet other embodiments, the analysis module combinesthe sample analyte concentrations algebraically to calculate a combinedsample analyte concentration that is compared to a combined minimumdiagnostic concentration calculated from the corresponding minimumdiagnostic criteria using the same algebraic operations. Othercombinations of sample analyte concentrations from within the same testsample, or combinations of sample analyte concentrations from two ormore different test samples containing two or more different bodilyfluids may be used to determine a particular renal disorder in stillother embodiments.

The system includes one or more processors and volatile and/ornonvolatile memory and can be embodied by or in one or more distributedor integrated components or systems. The system may include computerreadable media (CRM) on which one or more algorithms, software, modules,data, and/or firmware is loaded and/or operates and/or which operates onthe one or more processors to implement the systems and methodsidentified herein. The computer readable media may include volatilemedia, nonvolatile media, removable media, non-removable media, and/orother media or mediums that can be accessed by a general purpose orspecial purpose computing device. For example, computer readable mediamay include computer storage media and communication media, includingbut not limited to computer readable media. Computer storage mediafurther may include volatile, nonvolatile, removable, and/ornon-removable media implemented in a method or technology for storage ofinformation, such as computer readable instructions, data structures,program modules, and/or other data. Communication media may, forexample, embody computer readable instructions, data structures, programmodules, algorithms, and/or other data, including but not limited to asor in a modulated data signal. The communication media may be embodiedin a carrier wave or other transport mechanism and may include aninformation delivery method. The communication media may include wiredand wireless connections and technologies and may be used to transmitand/or receive wired or wireless communications. Combinations and/orsub-combinations of the above and systems, components, modules, andmethods and processes described herein may be made.

The following examples are included to demonstrate preferred embodimentsof the invention.

EXAMPLES

The following examples illustrate various iterations of the invention.

Example 1: Least Detectable Dose and Lower Limit of Quantitation ofAssay for Analytes Associated with Renal Disorders

To assess the least detectable doses (LDD) and lower limits ofquantitation (LLOQ) of a variety of analytes associated with renaldisorders, the following experiment was conducted. The analytes measuredwere alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin,clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN),THP, TIMP-1, TFF-3, and VEGF.

The concentrations of the analytes were measured using acapture-sandwich assay using antigen-specific antibodies. For eachanalyte, a range of standard sample dilutions ranging over about fourorders of magnitude of analyte concentration were measured using theassay in order to obtain data used to construct a standard dose responsecurve. The dynamic range for each of the analytes, defined herein as therange of analyte concentrations measured to determine its dose responsecurve, is presented below.

To perform the assay, 5 μL of a diluted mixture of capture-antibodymicrospheres were mixed with 5 μL of blocker and 10 μL of pre-dilutedstandard sample in each of the wells of a hard-bottom microtiter plate.After incubating the hard-bottom plate for 1 hour, 10 μL of biotinylateddetection antibody was added to each well, and then the hard-bottomplate was incubated for an additional hour. 10 μL of dilutedstreptavidin-phycoerythrin was added to each well and then thehard-bottom plate was incubated for another 60 minutes.

A filter-membrane microtiter plate was pre-wetted by adding 100 μL washbuffer, and then aspirated using a vacuum manifold device. The contentsof the wells of the hard-bottom plate were then transferred to thecorresponding wells of the filter-membrane plate. All wells of thehard-bottom plate were vacuum-aspirated and the contents were washedtwice with 100 μL of wash buffer. After the second wash, 100 μL of washbuffer was added to each well, and then the washed microspheres wereresuspended with thorough mixing. The plate was then analyzed using aLuminex 100 Analyzer (Luminex Corporation, Austin, Tex., USA). Doseresponse curves were constructed for each analyte by curve-fitting themedian fluorescence intensity (MFI) measured from the assays of dilutedstandard samples containing a range of analyte concentrations.

The least detectable dose (LDD) was determined by adding three standarddeviations to the average of the MFI signal measured for 20 replicatesamples of blank standard solution (i.e. standard solution containing noanalyte). The MFI signal was converted to an LDD concentration using thedose response curve and multiplied by a dilution factor of 2.

The lower limit of quantification (LLOQ), defined herein as the point atwhich the coefficient of variation (CV) for the analyte measured in thestandard samples was 30%, was determined by the analysis of themeasurements of increasingly diluted standard samples. For each analyte,the standard solution was diluted by 2 fold for 8 dilutions. At eachstage of dilution, samples were assayed in triplicate, and the CV of theanalyte concentration at each dilution was calculated and plotted as afunction of analyte concentration. The LLOQ was interpolated from thisplot and multiplied by a dilution factor of 2.

The LDD and LLOQ results for each analyte are summarized in Table 2:

TABLE 2 LDD, LLOQ, and Dynamic Range of Analyte Assay Dynamic RangeAnalyte Units LDD LLOQ minimum maximum Calbindin ng/mL 1.1 3.1 0.5162580 Clusterin ng/mL 2.4 2.3 0.676 3378 CTGF ng/mL 1.3 3.8 0.0794 400GST-alpha ng/mL 1.4 3.6 0.24 1,200 KIM-1 ng/mL 0.016 0.028 0.00478 24VEGF pg/mL 4.4 20 8.76 44,000 β-2 M μg/mL 0.012 0.018 0.0030 15 CystatinC ng/mL 2.8 3.7 0.60 3,000 NGAL ng/mL 4.1 7.8 1.2 6,000 Osteopontinng/mL 29 52 3.9 19,500 TIMP-1 ng/mL 0.71 1.1 0.073 365 A-1 M μg/mL 0.0590.29 0.042 210 THP μg/mL 0.46 0.30 0.16 800 TFF-3 μg/mL 0.06 0.097 0.060300

The results of this experiment characterized the least detectible doseand the lower limit of quantification for fourteen analytes associatedwith various renal disorders using a capture-sandwich assay.

Example 2: Precision of Assay for Analytes Associated with RenalDisorders

To assess the precision of an assay used to measure the concentration ofanalytes associated with renal disorders, the following experiment wasconducted. The analytes measured were alpha-1 microglobulin (A1M),beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C,GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.For each analyte, three concentration levels of standard solution weremeasured in triplicate during three runs using the methods described inExample 1. The percent errors for each run at each concentration arepresented in Table 3 for all of the analytes tested:

TABLE 3 Precision of Analyte Assay Average Run 1 Run 2 Run 2 Interrunconcentration Error Error Error Error Analyte (ng/mL) (%) (%) (%) (%)Calbindin 4.0 6 2 6 13 36 5 3 2 7 281 1 6 0 3 Clusterin 4.4 4 9 2 6 39 51 6 8 229 1 3 0 2 CTGF 1.2 10 17 4 14 2.5 19 19 14 14 18 7 5 13 9GST-alpha 3.9 14 7 5 10 16 13 7 10 11 42 1 16 6 8 KIM-1 0.035 2 0 5 130.32 4 5 2 8 2.9 0 5 7 4 VEGF 65 10 1 6 14 534 9 2 12 7 5,397 1 13 14 9β-2 M 0.040 6 1 8 5 0.43 2 2 0 10 6.7 6 5 11 6 Cystatin C 10.5 4 1 7 1349 0 0 3 9 424 2 6 2 5 NGAL 18.1 11 3 6 13 147 0 0 6 5 1,070 5 1 2 5Osteopontin 44 1 10 2 11 523 9 9 9 7 8,930 4 10 1 10 TIMP-1 2.2 13 6 313 26 1 1 4 14 130 1 3 1 4 A-1 M 1.7 11 7 7 14 19 4 1 8 9 45 3 5 2 4 THP9.4 3 10 11 11 15 3 7 8 6 37 4 5 0 5 TFF-3 0.3 13 3 11 12 4.2 5 8 5 71.2 3 7 0 13

The results of this experiment characterized the precision of acapture-sandwich assay for fourteen analytes associated with variousrenal disorders over a wide range of analyte concentrations. Theprecision of the assay varied between about 1% and about 15% errorwithin a given run, and between about 5% and about 15% error betweendifferent runs. The percent errors summarized in Table 2 provideinformation concerning random error to be expected in an assaymeasurement caused by variations in technicians, measuring instruments,and times of measurement.

Example 3: Linearity of Assay for Analytes Associated with RenalDisorders

To assess the linearity of an assay used to measure the concentration ofanalytes associated with renal disorders, the following experiment wasconducted. The analytes measured were alpha-1 microglobulin (A1M),beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C,GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.For each analyte, three concentration levels of standard solution weremeasured in triplicate during three runs using the methods described inExample 1. Linearity of the assay used to measure each analyte wasdetermined by measuring the concentrations of standard samples that wereserially-diluted throughout the assay range. The % recovery wascalculated as observed vs. expected concentration based on thedose-response curve. The results of the linearity analysis aresummarized in Table 4.

TABLE 4 Linearity of Analyte Assay Expected Observed Recovery AnalyteDilution concentration concentration (%) Calbindin 1:2 61 61 100 (ng/mL)1:4 30 32 106 1:8 15 17 110 Clusterin 1:2 41 41 100 (ng/mL) 1:4 21 24116 1:8 10 11 111 CTGF 1:2 1.7 1.7 100 (ng/mL) 1:4 0.84 1.0 124 1:8 0.420.51 122 GST-alpha 1:2 25 25 100 (ng/mL) 1:4 12 14 115 1:8 6.2 8.0 129KIM-1 1:2 0.87 0.87 100 (ng/mL) 1:4 0.41 0.41 101 1:8 0.21 0.19 93 VEGF1:2 2,525 2,525 100 (pg/mL) 1:4 1,263 1,340 106 1:8 631 686 109 β-2 M 1:100 0.63 0.63 100 (μg/mL)  1:200 0.31 0.34 106  1:400 0.16 0.17 107Cystatin C  1:100 249 249 100 (ng/mL)  1:200 125 122 102  1:400 62 56110 NGAL  1:100 1,435 1,435 100 (ng/mL)  1:200 718 775 108  1:400 359369 103 Osteopontin  1:100 6,415 6,415 100 (ng/mL)  1:200 3,208 3,275102  1:400 1,604 1,525 95 TIMP-1  1:100 35 35 100 (ng/mL)  1:200 18 18100  1:400 8.8 8.8 100 A-1 M   1:2000 37 37 100 (μg/mL)   1:4000 18 1899   1:8000 9.1 9.2 99 THP   1:2000 28 28 100 (μg/mL)   1:4000 14 14 96  1:8000 6.7 7.1 94 TFF-3   1:2000 8.8 8.8 100 (μg/mL)   1:4000 3.8 4.486   1:8000 1.9 2.2 86

The results of this experiment demonstrated reasonably linear responsesof the sandwich-capture assay to variations in the concentrations of theanalytes in the tested samples.

Example 4: Spike Recovery of Analytes Associated with Renal Disorders

To assess the recovery of analytes spiked into urine, serum, and plasmasamples by an assay used to measure the concentration of analytesassociated with renal disorders, the following experiment was conducted.The analytes measured were alpha-1 microglobulin (A1M), beta-2microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha,KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. For eachanalyte, three concentration levels of standard solution were spikedinto known urine, serum, and plasma samples. Prior to analysis, allurine samples were diluted 1:2000 (sample: diluent), all plasma sampleswere diluted 1:5 (sample: diluent), and all serum samples were diluted1:2000 (sample: diluent).

The concentrations of the analytes in the samples were measured usingthe methods described in Example 1. The average % recovery wascalculated as the proportion of the measurement of analyte spiked intothe urine, serum, or plasma sample (observed) to the measurement ofanalyte spiked into the standard solution (expected). The results of thespike recovery analysis are summarized in Table 5.

TABLE 5 Spike Recovery of Analyte Assay in Urine, Serum, and PlasmaSamples Recovery Recovery Recovery Spike in Urine in Serum in PlasmaAnalyte Concentration Sample (%) Sample (%) Sample (%) Calbindin 66 7682 83 (ng/mL) 35 91 77 71 18 80 82 73 average 82 80 76 Clusterin 80 7273 75 (ng/mL) 37 70 66 72 20 90 73 70 average 77 70 72 CTGF 8.4 91 80 79(ng/mL) 4.6 114 69 78 2.4 76 80 69 average 94 77 75 GST-alpha 27 75 8480 (ng/mL) 15 90 75 81 7.1 82 84 72 average 83 81 78 KIM-1 0.63 87 80 83(ng/mL) .029 119 74 80 0.14 117 80 78 average 107 78 80 VEGF 584 88 8482 (pg/mL) 287 101 77 86 123 107 84 77 average 99 82 82 β-2 M 0.97 11798 98 (μg/mL) 0.50 124 119 119 0.24 104 107 107 average 115 108 105Cystatin C 183 138 80 103 (ng/mL) 90 136 97 103 40 120 97 118 average131 91 108 NGAL 426 120 105 111 (ng/mL) 213 124 114 112 103 90 99 113average 111 106 112 Osteopontin 1,245 204 124 68 (ng/mL) 636 153 112 69302 66 103 67 average 108 113 68 TIMP-1 25 98 97 113 (ng/mL) 12 114 89103 5.7 94 99 113 average 102 95 110 A-1 M 0.0028 100 101 79 (μg/mL)0.0012 125 80 81 0.00060 118 101 82 Average 114 94 81 THP 0.0096 126 10890 (μg/mL) 0.0047 131 93 91 0.0026 112 114 83 average 123 105 88 TFF-30.0038 105 114 97 (μg/mL) 0.0019 109 104 95 0.0010 102 118 93 average105 112 95

The results of this experiment demonstrated that the sandwich-type assayis reasonably sensitive to the presence of all analytes measured,whether the analytes were measured in standard samples, urine samples,plasma samples, or serum samples.

Example 5: Matrix Interferences of Analytes Associated with RenalDisorders

To assess the matrix interference of hemoglobin, bilirubin, andtriglycerides spiked into standard samples, the following experiment wasconducted. The analytes measured were alpha-1 microglobulin (A1M),beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C,GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.For each analyte, three concentration levels of standard solution werespiked into known urine, serum, and plasma samples. Matrix interferencewas assessed by spiking hemoglobin, bilirubin, and triglyceride intostandard analyte samples and measuring analyte concentrations using themethods described in Example 1. A % recovery was determined bycalculating the ratio of the analyte concentration measured from thespiked sample (observed) divided by the analyte concentration measuredform the standard sample (expected). The results of the matrixinterference analysis are summarized in Table 6.

TABLE 6 Matrix Interference of Hemoglobin, Bilirubin, and Triglycerideon the Measurement of Analytes Matrix Compound Maximum Spike OverallAnalyte Spiked into Sample Concentration Recovery (%) CalbindinHemoglobin 500 110 (mg/mL) Bilirubin 20 98 Triglyceride 500 117Clusterin Hemoglobin 500 125 (mg/mL) Bilirubin 20 110 Triglyceride 50085 CTGF Hemoglobin 500 91 (mg/mL) Bilirubin 20 88 Triglyceride 500 84GST-alpha Hemoglobin 500 100 (mg/mL) Bilirubin 20 96 Triglyceride 500 96KIM-1 Hemoglobin 500 108 (mg/mL) Bilirubin 20 117 Triglyceride 500 84VEGF Hemoglobin 500 112 (mg/mL) Bilirubin 20 85 Triglyceride 500 114 β-2M Hemoglobin 500 84 (μg/mL) Bilirubin 20 75 Triglyceride 500 104Cystatin C Hemoglobin 500 91 (ng/mL) Bilirubin 20 102 Triglyceride 500124 NGAL Hemoglobin 500 99 (ng/mL) Bilirubin 20 92 Triglyceride 500 106Osteopontin Hemoglobin 500 83 (ng/mL) Bilirubin 20 86 Triglyceride 500106 TIMP-1 Hemoglobin 500 87 (ng/mL) Bilirubin 20 86 Triglyceride 500 93A-1 M Hemoglobin 500 103 (μg/mL) Bilirubin 20 110 Triglyceride 500 112THP Hemoglobin 500 108 (μg/mL) Bilirubin 20 101 Triglyceride 500 121TFF-3 Hemoglobin 500 101 (μg/mL) Bilirubin 20 101 Triglyceride 500 110

The results of this experiment demonstrated that hemoglobin, bilirubin,and triglycerides, three common compounds found in urine, plasma, andserum samples, did not significantly degrade the ability of thesandwich-capture assay to detect any of the analytes tested.

Example 6: Sample Stability of Analytes Associated with Renal Disorders

To assess the ability of analytes spiked into urine, serum, and plasmasamples to tolerate freeze-thaw cycles, the following experiment wasconducted. The analytes measured were alpha-1 microglobulin (A1M),beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C,GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.Each analyte was spiked into known urine, serum, and plasma samples at aknown analyte concentration. The concentrations of the analytes in thesamples were measured using the methods described in Example 1 after theinitial addition of the analyte, and after one, two and three cycles offreezing and thawing. In addition, analyte concentrations in urine,serum and plasma samples were measured immediately after the addition ofthe analyte to the samples as well as after storage at room temperaturefor two hours and four hours, and after storage at 4° C. for 2 hours,four hours, and 24 hours.

The results of the freeze-thaw stability analysis are summarized inTable 7. The % recovery of each analyte was calculated as a percentageof the analyte measured in the sample prior to any freeze-thaw cycles.

TABLE 7 Freeze-Thaw Stability of the Analytes in Urine, Serum, andPlasma Period Urine Sample Serum Sample Plasma Sample and RecoveryRecovery Recovery Analyte Temp Concentration (%) Concentration (%)Concentration (%) Calbindin Control 212 100 31 100 43 100 (ng/mL) 1X 221104 30 96 41 94 2X 203 96 30 99 39 92 3X 234 110 30 97 40 93 Clusterin 0315 100 232 100 187 100 (ng/mL) 1X 329 104 227 98 177 95 2X 341 108 240103 175 94 3X 379 120 248 107 183 98 CTGF 0 6.7 100 1.5 100 1.2 100(ng/mL) 1X 7.5 112 1.3 82 1.2 94 2X 6.8 101 1.4 90 1.2 100 3X 7.7 1151.2 73 1.3 107 GST- 0 12 100 23 100 11 100 alpha 1X 13 104 24 105 11 101(ng/mL) 2X 14 116 21 92 11 97 3X 14 111 23 100 12 108 KIM-1 0 1.7 1000.24 100 0.24 100 (ng/mL) 1X 1.7 99 0.24 102 0.22 91 2X 1.7 99 0.22 940.19 78 3X 1.8 107 0.23 97 0.22 93 VEGF 0 1,530 100 1,245 100 674 100(pg/mL) 1X 1,575 103 1,205 97 652 97 2X 1,570 103 1,140 92 612 91 3X1,700 111 1,185 95 670 99 β-2M 0 0.0070 100 1.2 100 15 100 (μg/mL) 1X0.0073 104 1.1 93 14 109 2X 0.0076 108 1.2 103 15 104 3X 0.0076 108 1.197 13 116 Cystatin C 0 1,240 100 1,330 100 519 100 (ng/mL) 1X 1,280 1031,470 111 584 113 2X 1,410 114 1,370 103 730 141 3X 1,420 115 1,380 104589 113 NGAL 0 45 100 245 100 84 100 (ng/mL) 1X 46 102 179 114 94 112 2X47 104 276 113 91 108 3X 47 104 278 113 91 109 Osteopontin 0 38 100 1.7100 5.0 100 (ng/mL) 1X 42 110 1.8 102 5.5 110 2X 42 108 1.5 87 5.5 1093X 42 110 1.3 77 5.4 107 TIMP-1 0 266 100 220 100 70 100 (ng/mL) 1X 265100 220 10 75 108 2X 255 96 215 98 77 110 3X 295 111 228 104 76 109 A-1M0 14 100 26 100 4.5 100 (μg/mL) 1X 13 92 25 96 4.2 94 2X 15 107 25 964.3 97 3X 16 116 23 88 4.0 90 THP 0 4.6 100 31 100 9.2 100 (μg/mL) 1X4.4 96 31 98 8.8 95 2X 5.0 110 31 100 9.2 100 3X 5.2 114 27 85 9.1 99TFF-3 0 4.6 100 24 100 22 100 (μg/mL) 1X 4.4 96 23 98 22 103 2X 5.0 11024 103 22 101 3X 5.2 114 19 82 22 102

The results of the short-term stability assessment are summarized inTable 8. The % recovery of each analyte was calculated as a percentageof the analyte measured in the sample prior to any short-term storage.

TABLE 8 Short-Term Stability of Analytes in Urine, Serum, and PlasmaStorage Urine Sample Serum Sample Plasma Sample Time/ Sample RecoverySample Recovery Sample Recovery Analyte Temp Conc. (%) Conc. (%) Conc.(%) Calbindin Control 226 100 33 100 7 100 (ng/mL) 2 hr/ 242 107 30 906.3 90 room temp 2 hr. @ 228 101 29 89 6.5 93 4° C. 4 hr @ 240 106 28 845.6 79 room temp 4 hr. @ 202 89 29 86 5.5 79 4° C. 24 hr. @ 199 88 26 787.1 101 4° C. Clusterin Control 185 100 224 100 171 100 (ng/mL) 2 hr @173 94 237 106 180 105 room temp 2 hr. @ 146 79 225 100 171 100 4° C. 4hr @ 166 89 214 96 160 94 room temp 4 hr. @ 157 85 198 88 143 84 4° C.24 hr. @ 185 100 207 92 162 94 4° C. CTGF Control 1.9 100 8.8 100 1.2100 (ng/mL) 2 hr @ 1.9 99 6.7 76 1 83 room temp 2 hr. @ 1.8 96 8.1 921.1 89 4° C. 4 hr @ 2.1 113 5.6 64 1 84 room temp 4 hr. @ 1.7 91 6.4 740.9 78 4° C. 24 hr. @ 2.2 116 5.9 68 1.1 89 4° C. GST- Control 14 100 21100 11 100 alpha 2 hr @ 11 75 23 107 11 103 (ng/mL) room temp 2 hr. @ 1393 22 104 9.4 90 4° C. 4 hr @ 11 79 21 100 11 109 room temp 4 hr. @ 1289 21 98 11 100 4° C. 24 hr. @ 13 90 22 103 14 129 4° C. KIM-1 Control1.5 100 0.23 100 0.24 100 (ng/mL) 2 hr @ 1.2 78 0.2 86 0.22 90 room temp2 hr. @ 1.6 106 0.23 98 0.21 85 4° C. 4 hr @ 1.3 84 0.19 82 0.2 81 roomtemp 4 hr. @ 1.4 90 0.22 93 0.19 80 4° C. 24 hr. @ 1.1 76 0.18 76 0.2394 4° C. VEGF Control 851 100 1215 100 670 100 (pg/mL) 2 hr @ 793 931055 87 622 93 room temp 2 hr. @ 700 82 1065 88 629 94 4° C. 4 hr @ 70483 1007 83 566 84 room temp 4 hr. @ 618 73 1135 93 544 81 4° C. 24 hr. @653 77 1130 93 589 88 4° C. β-2M Control 0.064 100 2.6 100 1.2 100(μg/mL) 2 hr @ 0.062 97 2.4 92 1.1 93 room temp 2 hr. @ 0.058 91 2.2 851.2 94 4° C. 4 hr @ 0.064 101 2.2 83 1.2 94 room temp 4 hr. @ 0.057 902.2 85 1.2 98 4° C. 24 hr. @ 0.06 94 2.5 97 1.3 103 4° C. Cystatin CControl 52 100 819 100 476 100 (ng/mL) 2 hr @ 50 96 837 102 466 98 roomtemp 2 hr. @ 44 84 884 108 547 115 4° C. 4 hr @ 49 93 829 101 498 105room temp 4 hr. @ 46 88 883 108 513 108 4° C. 24 hr. @ 51 97 767 94 47199 4° C. NGAL Control 857 100 302 100 93 100 (ng/mL) 2 hr @ 888 104 28795 96 104 room temp 2 hr. @ 923 108 275 91 92 100 4° C. 4 hr @ 861 101269 89 88 95 room temp 4 hr. @ 842 98 283 94 94 101 4° C. 24 hr. @ 960112 245 81 88 95 4° C. Osteopontin Control 2243 100 6.4 100 5.2 100(ng/mL) 2 hr @ 2240 100 6.8 107 5.9 114 room temp 2 hr. @ 2140 95 6.4101 6.2 120 4° C. 4 hr @ 2227 99 6.9 108 5.8 111 room temp 4 hr. @ 212095 7.7 120 5.2 101 4° C. 24 hr. @ 2253 100 6.5 101 6 116 4° C. TIMP-1Control 17 100 349 100 72 100 (ng/mL) 2 hr @ 17 98 311 89 70 98 roomtemp 2 hr. @ 16 94 311 89 68 95 4° C. 4 hr @ 17 97 306 88 68 95 roomtemp 4 hr. @ 16 93 329 94 74 103 4° C. 24 hr. @ 18 105 349 100 72 100 4°C. A-1M Control 3.6 100 2.2 100 1 100 (μg/mL) 2 hr @ 3.5 95 2 92 1 105room temp 2 hr. @ 3.4 92 2.1 97 0.99 99 4° C. 4 hr @ 3.2 88 2.2 101 0.9996 room temp 4 hr. @ 3 82 2.2 99 0.97 98 4° C. 24 hr. @ 3 83 2.2 100 1101 4° C. THP Control 1.2 100 34 100 2.1 100 (μg/mL) 2 hr @ 1.2 99 34 992 99 room temp 2 hr. @ 1.1 90 34 100 2 98 4° C. 4 hr @ 1.1 88 27 80 2 99room temp 4 hr. @ 0.95 79 33 97 2 95 4° C. 24 hr. @ 0.91 76 33 98 2.4116 4° C. TFF-3 Control 1230 100 188 100 2240 100 (μg/mL) 2 hr @ 1215 99179 95 2200 98 room temp 2 hr. @ 1200 98 195 104 2263 101 4° C. 4 hr @1160 94 224 119 2097 94 room temp 4 hr. @ 1020 83 199 106 2317 103 4° C.24 hr. @ 1030 84 229 122 1940 87 4° C.

The results of this experiment demonstrated that the analytes associatedwith renal disorders tested were suitably stable over severalfreeze/thaw cycles, and up to 24 hrs of storage at a temperature of 4°C.

Example 8: Analysis of Kidney Biomarkers in Plasma and Urine fromPatients with Renal Injury

A screen for potential protein biomarkers in relation to kidneytoxicity/damage was performed using a panel of biomarkers, in a set ofurine and plasma samples from patients with documented renal damage. Theinvestigated patient groups included diabetic nephropathy (DN),obstructive uropathy (OU), analgesic abuse (AA) and glomerulonephritis(GN) along with age, gender and BMI matched control groups. Multiplexedimmunoassays were applied in order to quantify the following proteinanalytes: Alpha-1 Microglobulin (α1M), KIM-1, Microalbumin,Beta-2-Microglobulin (β2M), Calbindin, Clusterin, CystatinC,TreFoilFactor-3 (TFF-3), CTGF, GST-alpha, VEGF, Calbindin, Osteopontin,Tamm-HorsfallProtein (THP), TIMP-1 and NGAL.

Li-Heparin plasma and mid-stream spot urine samples were collected fromfour different patient groups. Samples were also collected from age,gender and BMI matched control subjects. 20 subjects were included ineach group resulting in a total number of 160 urine and plasma samples.All samples were stored at −80° C. before use. Glomerular filtrationrate for all samples was estimated using two different estimations(Modification of Diet in Renal Disease or MDRD, and the Chronic KidneyDisease Epidemiology Collaboration or CKD-EPI) to outline the eGFR(estimated glomerular filtration rate) distribution within each patientgroup (FIG. 1). Protein analytes were quantified in human plasma andurine using multiplexed immunoassays in the Luminex xMAP™ platform. Themicrosphere-based multiplex immunoassays consist of antigen-specificantibodies and optimized reagents in a capture-sandwich format. Outputdata was given as g/ml calculated from internal standard curves. Becauseurine creatinine (uCr) correlates with renal filtration rate, dataanalysis was performed without correction for uCr. Univariate andmultivariate data analysis was performed comparing all case vs. controlsamples as well as cases vs. control samples for the various diseasegroups.

The majority of the measured proteins showed a correlation to eGFR.Measured variables were correlated to eGFR using Pearson's correlationscoefficient, and samples from healthy controls and all disease groupswere included in the analysis. 11 and 7 proteins displayed P-valuesbelow 0.05 for plasma and urine (Table 9) respectively.

TABLE 9 Correlation analysis of eGFR and variables for all case samplesURINE PLASMA Variable Pearson's r P-Value Variable Pearson's r P-ValueAlpha-1- −0.08 0.3 Alpha-1- −0.33

Microglobulin Microglobulin Beta-2- −0.23 0.003 Beta-2- −0.39

Microglobulin Microglobulin Calbindin −0.16 0.04 Calbindin −0.18 <0.02Clusterin −0.07 0.4 Clusterin −0.51

CTGF −0.08 0.3 CTGF −0.05 0.5 Creatinine −0.32

Cystatin-C −0.42 <0.0001 Cystatin-C −0.24 0.002 GST-alpha −0.12 0.1GST-alpha −0.11 0.2 KIM-1 −0.17 0.03 KIM-1 −0.08 0.3 NGAL −0.28 <0.001Microalbumin_UR −0.17 0.03 Osteopontin −0.33

NGAL −0.15 0.07 THP −0.31

Osteopontin −0.19 0.02 TIMP-1 −0.28 <0.001 THP −0.05 0.6 TFF3 −0.38

TIMP-1 −0.19 0.01 VEGF −0.14 0.08 TFF2 −0.09 0.3 VEGF −0.07 0.4 P values<0.0001 are shown in bold italics P values <0.005 are shown in bold Pvalues <0.05 are shown in italics

For the various disease groups, univariate statistical analysis revealedthat in a direct comparison (T-test) between cases and controls, anumber of proteins were differentially expressed in both urine andplasma (Table 10 and FIG. 2). In particular, clusterin showed a markeddifferential pattern in plasma.

TABLE 10 Differentially regulated proteins by univariate statisticalanalysis Group Matrix Protein p-value AA Urine Calbindin 0.016 AA UrineNGAL 0.04 AA Urine Osteopontin 0.005 AA Urine Creatinine 0.001 AA PlasmaCalbindin 0.05 AA Plasma Clusterin 0.003 AA Plasma KIM-1 0.03 AA PlasmaTHP 0.001 AA Plasma TIMP-1 0.02 DN Urine Creatinine 0.04 DN PlasmaClusterin 0.006 DN Plasma KIM-1 0.01 GN Urine Creatinine 0.004 GN UrineMicroalbumin 0.0003 GN Urine NGAL 0.05 GN Urine Osteopontin 0.05 GNUrine TFF3 0.03 GN Plasma Alpha 1 Microglobulin 0.002 GN Plasma Beta 2Microglobulin 0.03 GN Plasma Clusterin 0.00 GN Plasma Cystatin C 0.01 GNPlasma KIM-1 0.003 GN Plasma NGAL 0.03 GN Plasma THP 0.001 GN PlasmaTIMP-1 0.003 GN Plasma TFF3 0.01 GN Plasma VEGF 0.02 OU Urine Clusterin0.02 OU Urine Microalbumin 0.007 OU Plasma Clusterin 0.00

Application of multivariate analysis yielded statistical models thatpredicted disease from control samples (plasma results are shown in FIG.3)

In conclusion, these results form a valuable base for further studies onthese biomarkers in urine and plasma both regarding baseline levels innormal populations and regarding the differential expression of theanalytes in various disease groups. Using this panel of analytes, errorrates from adaboosting and/or random forest were low enough (<10%) toallow a prediction model to differentiate between control and diseasepatient samples. Several of the analytes showed a greater correlation toeGFR in plasma than in urine.

Example 9: Statistical Analysis of Kidney Biomarkers in Plasma and Urinefrom Patients with Renal Injury

Urine and plasma samples were taken from 80 normal control groupsubjects and 20 subjects from each of four disorders: analgesic abuse,diabetic nephropathy, glomerulonephritis, and obstructive uropathy. Thesamples were analyzed for the quantity and presence of 16 differentproteins (alpha-1 microglobulin (α1M), beta-2 microglobulin (β2M),calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1,microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF) asdescribed in Example 1 above. The goal was to determine the analytesthat distinguish between a normal sample and a diseased sample, a normalsample and a diabetic nephropathy (DN) sample, and finally, an diabeticnephropathy sample from the other disease samples (obstructive uropathy(DN), analgesic abuse (AA), and glomerulonephritis (GN)).

From the above protein analysis data, bootstrap analysis was used toestimate the future performance of several classification algorithms.For each bootstrap run, training data and testing data was randomlygenerated. Then, the following algorithms were applied on the trainingdata to generate models and then apply the models to the testing data tomake predictions: automated Matthew's classification algorithm,classification and regression tree (CART), conditional inference tree,bagging, random forest, boosting, logistic regression, SVM, and Lasso.The accuracy rate and ROC areas were recorded for each method on theprediction of the testing data. The above was repeated 100 times. Themean and the standard deviation of the accuracy rates and of the ROCareas were calculated.

The mean error rates and AUROC were calculated from urine and AUROC wascalculated from plasma for 100 runs of the above method for each of thefollowing comparisons: disease (AA+GN+OU+DN) vs. normal (FIG. 4, Table11), DN vs. normal (FIG. 6, Table 13), DN vs. AA (FIG. 8, Table 15), OUvs. DN (FIG. 10, Table 17), and GN vs. DN (FIG. 12, Table 19).

The average relative importance of 16 different analytes (alpha-1microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF,creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL,osteopontin, THP, TIMP-1, TFF-3, and VEGF) and 4 different clinicalvariables (weight, BMI, age, and gender) from 100 runs were analyzedwith two different statistical methods—random forest (plasma and urinesamples) and boosting (urine samples)—for each of the followingcomparisons: disease (AA+GN+OU+DN) vs. normal (FIG. 5, Table 12), DN vs.normal (FIG. 7, Table 14), DN vs. AA (FIG. 9, Table 16), OU vs. DN (FIG.11, Table 18), and GN vs. DN (FIG. 13, Table 20).

TABLE 11 Disease v. Normal Standard Mean deviation method AUROC AUROCrandom forest 0.931 0.039 bagging 0.919 0.045 svm 0.915 0.032 boosting0.911 0.06 lasso 0.897 0.044 logistic regression 0.891 0.041 ctree 0.8470.046 cart 0.842 0.032 matt 0.83 0.023

TABLE 12 Disease v. Normal analyte relative importance Creatinine 11.606Kidney_Injury_M 8.486 Tamm_Horsfall_P 8.191 Total_Protein 6.928Osteopontin 6.798 Neutrophil_Gela 6.784 Tissue_Inhibito 6.765Vascular_Endoth 6.716 Trefoil_Factor_(—) 6.703 Cystatin_C 6.482Alpha_1_Microgl 6.418 Beta_2_Microglo 6.228 Glutathione_S_T 6.053clusterin 5.842

TABLE 13 DN v. NL Standard Mean deviation method AUROC AUROC svm 0.6720.102 logistic regression 0.668 0.114 random forest 0.668 0.1 boosting0.661 0.107 lasso 0.66 0.117 bagging 0.654 0.103 matt 0.642 0.087 cart0.606 0.088 ctree 0.569 0.091

TABLE 14 DN v. NL analyte Relative importance Kidney_Injury_M 8.713Tamm_Horsfall_P 8.448 Beta_2_Microglo 8.037 Trefoil_Factor_(—) 7.685clusterin 7.394 Vascular_Endoth 7.298 Alpha_1_Microgl 6.987Glutathione_S_T 6.959 Cystatin_C 6.920 Tissue_Inhibito 6.511 Creatinine6.344 Neutrophil_Gela 6.305 Osteopontin 6.265 Total_Protein 6.133

TABLE 15 DN v. AA Standard Mean deviation method AUROC AUROC lasso 0.9990.008 random forest 0.989 0.021 svm 0.988 0.039 boosting 0.988 0.022bagging 0.972 0.036 logistic regression 0.969 0.057 cart 0.93 0.055ctree 0.929 0.063 matt 0.862 0.12

TABLE 16 DN v. AA analyte Relative importance Creatinine 17.57Total_Protein 10.90 Tissue_Inhibito 8.77 clusterin 6.89 Glutathione_S_T6.24 Alpha_1_Microgl 6.15 Beta_2_Microglo 6.06 Cystatin_C 5.99Trefoil_Factor_(—) 5.88 Kidney_Injury_M 5.49 Vascular_Endoth 5.38Tamm_Horsfall_P 5.33 Osteopontin 4.86 Neutrophil_Gela 4.47

TABLE 17 OU v. DN method mean_AUROC std_AUROC lasso 0.993 0.019 randomforest 0.986 0.027 boosting 0.986 0.027 bagging 0.977 0.04 cart 0.9620.045 ctree 0.954 0.05 svm 0.95 0.059 logistic regression 0.868 0.122matt 0.862 0.111

TABLE 18 OU v. DN analyte Relative importance Creatinine 18.278Alpha_1_Microgl 9.808 clusterin 9.002 Beta_2_Microglo 8.140 Cystatin_C7.101 Osteopontin 6.775 Glutathione_S_T 5.731 Neutrophil_Gela 5.720Trefoil_Factor_(—) 5.290 Kidney_Injury_M 5.031 Total_Protein 5.030Vascular_Endoth 4.868 Tissue_Inhibito 4.615 Tamm_Horsfall_P 4.611

TABLE 19 GN v. DN Standard Mean deviation of method AUROC AUROC lasso0.955 0.077 random forest 0.912 0.076 bagging 0.906 0.087 boosting 0.9040.087 svm 0.887 0.089 ctree 0.824 0.095 matt 0.793 0.114 logisticregression 0.788 0.134 cart 0.768 0.1

TABLE 20 GN v. DN analyte Relative importance Total_Protein 13.122Creatinine 11.028 Alpha_1_Microgl 8.291 Beta_2_Microglo 7.856Tissue_Inhibito 7.799 Glutathione_S_T 6.532 Kidney_Injury_M 6.489Osteopontin 6.424 Vascular_Endoth 6.262 Neutrophil_Gela 5.418Trefoil_Factor_(—) 5.382 Cystatin_C 5.339 Tamm_Horsfall_P 5.117clusterin 4.940

Example 10: Diabetic Kidney Disease Urine Analyte Analyses

Collaborators from Texas Diabetes and Endocrinology (H1) provided urinesamples for 150 patients with diabetes, of which 75 patients had kidneydisease and 75 did not. The samples were analyzed using the sixteenanalytes detailed in section I above. The goals of the analyses were asfollows: 1) Determine if there are analytes (alone or in combination)that can separate patients with kidney disease from patients withoutkidney disease (controls); 2) Determine the relationships of analytesand kidney disease category to years since diagnosis, age, gender, andBMI.

Values of <LOW> were replaced by half of the minimum value for eachvariable. Variables with more than 50% missing values were not analyzed.Values given as ‘>nnn’ were taken as the “nnn” value following the “>”sign.

Analyte values were normalized to the urine creatinine value in thepanel for each patient. Normalized value=100*the original analyte valuedivided by the creatinine value.

The distribution of values for most analytes was skewed, so the originalvalues were log transformed. Analyses were performed using both theoriginal values and the log transformed values.

In the graphs and statistical output, patients without kidney diseaseare labeled “NC” (normal control). Patients with kidney disease arelabeled “KD” (kidney disease).

Graphs of the analyte values versus disease category (NC vs. KD) onoriginal scale and log scale are shown in FIG. 22 and FIG. 23. Normaldistribution qqplots are shown in FIG. 20 and FIG. 21. Scatterplots ofeach analyte versus the 24-hour microalbumin (from the clinical data)are shown FIG. 16 and FIG. 17. A graph of the kidney disease categoryversus years since diagnosis and of analyte values versus years sincediagnosis are in FIG. 14, FIG. 15, and FIG. 24. In these graphs, red arepatients with kidney disease, black are controls. It is evident that thepresence of kidney disease is a function of years since diagnosis. Thus,models to predict kidney disease may perform better if the number ofyears since diagnosis is included as a covariate.

We performed t-tests of the values of each analyte versus diseasecategory (NC vs. KD). Linear models of analyte versus disease categoryand covariates gave similar results.

TABLE 21 T-test p-values for each analyte versus disease category (NCvs. KD) using log scale. t-test Analytes p-value Microalbumin 2.68E−21Alpha.1.Microglobulin 1.29E−05Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.004Kidney.Injury.Molecule.1 . . . KIM.1. 0.024 Clusterin 0.037Tamm.Horsfall.Protein . . . THP. 0.041 Connective.Tissue.Growth.Factor .. . CTGF. 0.044 Tissue.Inhibitor.of.Metalloproteinase.1 . . . TIMP.1.0.180 Beta.2.Microglobulin 0.334 Cystatin.C 0.348 Osteopontin 0.352Vascular.Endothelial.Growth.Factor . . . VEGF. 0.426 Creatinine 0.567Calbindin 0.707 Glutathione.S.Transferase.alpha . . . GST.alpha. 0.863Trefoil.Factor.3 . . . TFF3. 0.878

TABLE 22 T-test p-values for each analyte versus disease category (NCvs. KD) using original scale. t-test Analytes p-value Microalbumin1.11E−08 Alpha.1.Microglobulin 0.0007 Kidney.Injury.Molecule.1 . . .KIM.1. 0.0072 Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL.0.0190 Osteopontin 0.1191 Glutathione.S.Transferase.alpha . . .GST.alpha. 0.1250 Beta.2.Microglobulin 0.1331 Tamm.Horsfall.Protein . .. THP. 0.1461 Cystatin.C 0.1489 Connective.Tissue.Growth.Factor . . .CTGF. 0.2746 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.3114Calbindin 0.6189 Tissue.Inhibitor.of.Metalloproteinase.1 . . . TIMP.1.0.6944 Clusterin 0.7901 Trefoil.Factor.3 . . . TFF3. 0.7918 Creatinine0.9710

We calculated the area under the ROC curve (AUROC) for classification ofdisease (NC vs. KD) for the following analytes and covariates: AUROC foreach analyte individually (Table 23) and AUROC for individual analytesin logistic regression models that included the covariates yeardiagnosed, age, gender, and BMI (Table 24).

TABLE 23 AUROC for each analyte individually for classification ofdisease (NC vs. KD) using log scale Analytes AUROC Microalbumin 0.90Alpha.1.Microglobulin 0.71 Kidney.Injury.Molecule.1 . . . KIM.1. 0.63Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.62 Clusterin0.61 Tamm.Horsfall.Protein . . . THP. 0.60Connective.Tissue.Growth.Factor . . . CTGF. 0.60Tissue.Inhibitor.of.Metalloproteinase.1 . . . TIMP.1. 0.58 Cystatin.C0.56 Osteopontin 0.56 Beta.2.Microglobulin 0.56Vascular.Endothelial.Growth.Factor . . . VEGF. 0.55 Creatinine 0.52Calbindin 0.51 Trefoil.Factor.3 . . . TFF3. 0.51Glutathione.S.Transferase.alpha . . . GST.alpha. 0.50

TABLE 24 AUROC for individual analytes in logistic regression modelsthat included the covariates year since diagnosis, age, gender, and BMI.Analytes AUROC Microalbumin 0.90 Alpha.1.Microglobulin 0.74Connective.Tissue.Growth.Factor . . . CTGF. 0.71Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.69Kidney.Injury.Molecule.1 . . . KIM.1. 0.69 Tamm.Horsfall.Protein . . .THP. 0.69 Creatinine 0.69 Tissue.Inhibitor.of.Metalloproteinase.1 . . .TIMP.1. 0.68 Clusterin 0.68 Glutathione.S.Transferase.alpha . . .GST.alpha. 0.68 Osteopontin 0.68 Calbindin 0.68 Trefoil.Factor.3 . . .TFF3. 0.68 Cystatin.C 0.67 Vascular.Endothelial.Growth.Factor . . .VEGF. 0.67 Beta.2.Microglobulin 0.67

We calculated the area under the ROC curve (AUROC) for classification ofdisease (NC vs. KD) for the following combinations of analytes andcovariates. For the combination of all analytes in a logistic regressionmodel (without covariates), the AUROC=0.94. For the combination of allanalytes in a logistic regression model (including covariates), theAUROC=0.95. For the combination of all analytes, excluding microalbumin,in a logistic regression model (without covariates), the AUROC=0.85. Forthe combination of all analytes, excluding microalbumin, in a logisticregression model (including covariates), the AUROC=0.87. Finally, wecalculated the area under the ROC curve (AUROC) for classification ofdisease (NC vs. KD) for 24-hour clinical microalbumin from the patientrecord, which gave AUROC=0.97.

Example 11: Diabetic Kidney Disease Serum Analyte Analyses

This report presents the statistical analysis of the serum data for thepatients detailed in Example 10 above. The samples were analzed usingfourteen of the analytes detailed in section I above. The goals of theanalyses were as follows: 1) Determine if there are analytes (alone orin combination) that can separate patients with kidney disease frompatients without kidney disease (controls); 2) Determine therelationships of analytes and kidney disease category to years sincediagnosis, age, gender, and BMI.

Values of <LOW> were replaced by half of the minimum value for eachvariable. Variables with more than 50% missing values were not analyzed.The only such analyte in this data set was Calbindin. Values given asnnn′ were taken as the “nnn” value following the “>” sign.

The distribution of values for most analytes was skewed, so we logtransformed the original values. We performed analyses using both theoriginal values and the log transformed values.

In the graphs and statistical output, patients without kidney diseaseare labeled “NC” (normal control). Patients with kidney disease arelabeled “KD” (kidney disease).

Graphs of the analyte values versus disease category (NC vs. KD) onoriginal scale and log scale are shown in FIG. 25 and FIG. 26. Normaldistribution qqplots are shown in FIG. 27 and FIG. 28. Scatterplots ofeach analyte versus the 24-hour microalbumin (from the clinical data)are shown in FIG. 31 and FIG. 32. Graphs of analyte values versus yearssince diagnosis are shown in FIG. 29 and FIG. 30. In these graphs, redare patients with kidney disease, black are controls. It is evident thatanalyte values and the presence of kidney disease is a function of yearssince diagnosis. Thus, models to predict kidney disease may performbetter if the number of years since diagnosis is included as acovariate.

We performed t-tests of the values of each analyte versus diseasecategory (NC vs. KD). Linear models of analyte versus disease categoryand covariates gave similar results.

TABLE 25 T-test p-values for each analyte versus disease category (NCvs. KD) using log scale. t-test Analytes p-value Alpha.1.Microglobulin .. . A1Micro. 8.03E−08 Cystatin.C 4.51E−06Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 5.35E−06Beta.2.Microglobulin . . . B2M. 3.88E−05Tissue.Inhibitor.of.Metalloproteinases.1 . . . TIMP.1. 4.20E−05Kidney.Injury.Molecule.1 . . . KIM.1. 0.00343048 Trefoil.Factor.3 . . .TFF3. 0.05044019 Connective.Tissue.Growth.Factor . . . CTGF. 0.06501133Glutathione.S.Transferase.alpha . . . GST.alpha. 0.27177709 Osteopontin0.2762483 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.33297341Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.5043943Clusterin . . . CLU. 0.5730406

TABLE 26 T-test p-values for each analyte versus disease category (NCvs. KD) using original scale. t-test Analytes p-valueAlpha.1.Microglobulin . . . A1Micro. 4.29E−07 Cystatin.C 5.52E−06Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 3.19E−05Beta.2.Microglobulin . . . B2M. 4.56E−05Tissue.Inhibitor.of.Metalloproteinases.1 . . . TIMP.1. 5.02E−05Kidney.Injury.Molecule.1 . . . KIM.1. 0.000343Vascular.Endothelial.Growth.Factor . . . VEGF. 0.044555Glutathione.S.Transferase.alpha . . . GST.alpha. 0.052145 Osteopontin0.146316 Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.21544Trefoil.Factor.3 . . . TFF3. 0.300221 Clusterin . . . CLU. 0.756401Connective.Tissue.Growth.Factor . . . CTGF. 0.985909

We calculated the area under the ROC curve (AUROC) for classification ofdisease (NC vs. KD) for the following analytes and covariates using logscale. AUROC for each analyte individually (Table 27) and AUROC forindividual analytes in logistic regression models that included thecovariates year diagnosed, age, gender, and BMI (Table 28).

TABLE 27 AUROC for each analyte individually for classification ofdisease (NC vs. KD) Analytes AUROC Alpha.1.Microglobulin . . . A1Micro.0.743154 Cystatin.C 0.705548 Tissue.Inhibitor.of.Metalloproteinases.1 .. . TIMP.1. 0.695857 Beta.2.Microglobulin . . . B2M. 0.693901Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 0.684566Kidney.Injury.Molecule.1 . . . KIM.1. 0.654783 Trefoil.Factor.3 . . .TFF3. 0.617977 Connective.Tissue.Growth.Factor . . . CTGF. 0.60144Glutathione.S.Transferase.alpha . . . GST.alpha. 0.549698 Osteopontin0.546497 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.541874Clusterin . . . CLU. 0.512002 Neutrophil.Gelatinase.Associated.Lipocalin. . . NGAL. 0.506312

TABLE 28 AUROC for individual analytes in logistic regression modelsthat included the covariates year since diagnosis, age, gender, and BMI.Analytes AUROC Alpha.1.Microglobulin . . . A1Micro. 0.760846 Cystatin.C0.731863 Tissue.Inhibitor.of.Metalloproteinases.1 . . . TIMP.1. 0.728841Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 0.725818Beta.2.Microglobulin . . . B2M. 0.718706 Kidney.Injury.Molecule.1 . . .KIM.1. 0.697724 Trefoil.Factor.3 . . . TFF3. 0.689189Connective.Tissue.Growth.Factor . . . CTGF. 0.682877Glutathione.S.Transferase.alpha . . . GST.alpha. 0.678165 Clusterin . .. CLU. 0.676565 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.674431Osteopontin 0.673898 Neutrophil.Gelatinase.Associated.Lipocalin . . .NGAL. 0.672653

We calculated the area under the ROC curve (AUROC) for classification ofdisease (NC vs. KD) for the following combinations of analytes andcovariates. For the combination of all analytes in a logistic regressionmodel (without covariates), the AUROC=0.85. For the combination of allanalytes in a logistic regression model (including covariates), theAUROC=0.86.

It should be appreciated by those of skill in the art that thetechniques disclosed in the examples above represent techniquesdiscovered by the inventors to function well in the practice of theinvention. Those of skill in the art should, however, in light of thepresent disclosure, appreciate that many changes can be made in thespecific embodiments that are disclosed and still obtain a like orsimilar result without departing from the spirit and scope of theinvention, therefore all matter set forth or shown in the accompanyingdrawings is to be interpreted as illustrative and not in a limitingsense.

What is claimed is:
 1. A method for generating a dataset, the methodcomprising: a. performing at least one immunoassay to generate sampleconcentrations for a combination of sample analytes in a test samplecomprising a sample of bodily fluid taken from a mammal, wherein themammal is suspected of having diabetic nephropathy or an associateddisorder, and wherein the combination of sample analytes comprises threeor more sample analytes selected from the group consisting of alpha-1microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF,creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL,osteopontin, THP, TIMP-1, TFF-3, and VEGF; b. comparing the combinationof sample concentrations to a data set comprising at least one entry,wherein each entry of the data set comprises a list comprising three ormore minimum diagnostic concentrations indicative of diabeticnephropathy or an associated disorder, wherein each minimum diagnosticconcentration comprises a maximum of a range of analyte concentrationsfor a healthy mammal; and c. generating a dataset by determining amatching entry of the dataset in which all minimum diagnosticconcentrations are less than the corresponding sample concentrations. 2.The method of claim 23, wherein the mammal is selected from the groupconsisting of humans, apes, monkeys, rats, mice, dogs, cats, pigs, andlivestock including cattle and oxen.
 3. The method of claim 23, whereinthe bodily fluid is selected from the group consisting of urine, blood,plasma, serum, saliva, semen, and tissue lysates.
 4. The method of claim23, wherein the minimum diagnostic concentration in human plasma ofalpha-1 microglobulin is about 16 μg/ml, beta-2 microglobulin is about2.2 μg/ml, calbindin is greater than about 5 ng/ml, clusterin is about134 μg/ml, CTGF is about 16 μg/ml, cystatin C is about 1170 ng/ml,GST-alpha is about 62 ng/ml, KIM-1 is about 0.57 ng/ml, NGAL is about375 ng/ml, osteopontin is about 25 ng/ml, THP is about 0.052 μg/ml,TIMP-1 is about 131 ng/ml, TFF-3 is about 0.49 μg/ml, and VEGF is about855 μg/ml.
 5. The method of claim 23, wherein a combination of sampleconcentrations for six or more sample analytes in the test sample aredetermined.
 6. The method of claim 27, wherein sample concentrations aredetermined for the analytes selected from the group consisting ofalpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, andTIMP-1.
 7. The method of claim 23, wherein a combination of sampleconcentrations for sixteen sample analytes in the test sample aredetermined.
 8. A method for generating a dataset, the method comprising:a. performing at least one immunoassay to generate sample concentrationsfor a combination of at least sixteen different sample analytes in atest sample comprising a sample of bodily fluid taken from a mammal,wherein the mammal is suspected of having diabetic nephropathy or anassociated disorder, and wherein the combination of the at least sixteendifferent sample analytes comprises each of alpha-1 microglobulin,beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatinC, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1,TFF-3, and VEGF; b. comparing the combination of sample concentrationsto a data set comprising at least one entry, wherein each entry of thedata set comprises a list comprising three or more minimum diagnosticconcentrations indicative of diabetic nephropathy or an associateddisorder, wherein each minimum diagnostic concentration comprises amaximum of a range of analyte concentrations for a healthy mammal; andc. generating a dataset by determining a matching entry of the datasetin which all minimum diagnostic concentrations are less than thecorresponding sample concentrations.